JP2004229313A - Storage, data processor and data processing method, program and recording medium, and data processing system - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To enhance the function of a television receiver additionally. <P>SOLUTION: An image processing card 13<SB>i</SB>stores creation information being used together with creation information stored in image processing cards 13<SB>1</SB>to 13<SB>i-1</SB>in order to obtain specified tap coefficients and that creation information is fed to an image processing interface 40. The image processing interface 40 creates tap coefficients from creation information in the image processing cards 13<SB>1</SB>to 13<SB>i</SB>set thereto. On the other hand, the image processing interface 40 extracts image data becoming a prediction tap being used for predicting a remarked pixel, and image data becoming a class tap being used for classifying the remarked pixels and then classifies the remarked pixels. Thereafter, the image processing interface 40 determines a remarked pixel from the tap coefficients of the class of remarked pixels and the prediction tap. <P>COPYRIGHT: (C)2004,JPO&NCIPI

Description

  The present invention relates to a storage device, a data processing device and a data processing method, a program and a recording medium, and a data processing system, and in particular, to a storage device that enables, for example, a television receiver to have additional functions. The present invention relates to an apparatus, a data processing device and a data processing method, a program and a recording medium, and a data processing system.

  For example, a television receiver receives a television broadcast signal, displays an image as a television broadcast program, and outputs sound accompanying the image.

  By the way, in recent years, the Y / C separation processing and other various signal processing in a television receiver have rapidly become sophisticated, and television receivers for performing more sophisticated signal processing have been developed one after another. Sold.

  However, conventionally, when a television receiver that performs signal processing with higher functions than the television receiver owned by the user is sold, the user is required to replace the television receiver unless the user replaces the television receiver. Could not enjoy the high functionality.

  On the other hand, for example, in a computer, a board having a predetermined function such as an image processing board or an audio processing board can be mounted. For example, an image processing board that receives a television broadcast and performs MPEG encoding is provided. By mounting it on a computer, the computer can record a television broadcast program in real time while MPEG encoding.

  Therefore, it is conceivable that the television receiver is configured so that a board for adding a new function can be mounted, similarly to the computer. In this case, the user can enjoy a new function without purchasing a new television receiver by purchasing a board for performing signal processing of a new function and mounting the board on a television receiver owned by the user. It becomes possible.

  However, if a board that performs signal processing for a certain function is sold and then a board that performs signal processing with a higher function is newly sold, the previously sold board becomes unnecessary.

  Therefore, as described above, in the present day when signal processing is rapidly becoming sophisticated, even if a board with a certain function is sold, a board with a higher function is sold immediately, so that the user can change the board. Incentives to buy will be less effective.

  On the other hand, if a previously sold board is needed to enjoy the function of a certain board, that is, by adding a newly sold board to the previously sold board, If the function of the machine is additionally enhanced, the incentive for the user to purchase the board is more likely to work.

  The present invention has been made in view of such a situation, and aims to additionally enhance the functions of a television receiver or the like.

  The first storage device of the present invention generates a tap coefficient for each predetermined class for performing a data conversion process of converting first data into second data having higher quality than the first data. Generation information storage means for storing generation information for generating, tap coefficient generation means for generating a tap coefficient from the generation information according to the control of the data processing device, and a target data of interest among the second data. A prediction tap extracting means for extracting a prediction tap used for prediction from the first data supplied from the data processing device; and a class classification for classifying the data of interest into one of a plurality of classes. Class tap extraction means for extracting the class tap used for the first processing from the first data supplied from the data processing device; And class classification means for scan classification, the tap coefficients of the class for the target data, and a prediction tap, predicts the target data, characterized by comprising a prediction means for supplying to the data processing device.

  According to a first data processing method of the present invention, a tap coefficient generating step of generating a tap coefficient from generated information and a target data of interest among the second data are predicted under the control of the data processing device. A prediction tap extraction step of extracting a prediction tap used in the first processing from the first data supplied from the data processing device, and a classification of classifying the data of interest into one of a plurality of classes. Class tap extracting step of extracting a class tap to be extracted from the first data supplied from the data processing device, a class classification step of classifying the target data based on the class tap, and a tap coefficient of a class for the target data And a prediction tap for predicting target data from the prediction tap and supplying the predicted data to the data processing device. It is characterized in.

  According to a first program of the present invention, a tap coefficient generation step of generating a tap coefficient from generation information under control of a data processing device, and predicting target data of interest among second data. A prediction tap extraction step of extracting a prediction tap to be used from first data supplied from a data processing device, and a class used to perform a class classification of classifying the data of interest into one of a plurality of classes. A class tap extracting step of extracting taps from the first data supplied from the data processing device, a class classification step of classifying the data of interest based on the class taps, a tap coefficient of a class for the data of interest, A prediction step of predicting target data from the prediction tap and supplying the predicted data to the data processing device. The features.

  According to the first recording medium of the present invention, according to the control of the data processing device, a tap coefficient generating step of generating a tap coefficient from the generation information, and predicting a target data of interest among the second data. Is used to perform a prediction tap extraction step of extracting a prediction tap used in the first data from the first data supplied from the data processing device, and a class classification of classifying the data of interest into one of a plurality of classes. A class tap extracting step of extracting a class tap from the first data supplied from the data processing device, a class classification step of classifying the data of interest based on the class tap, and a tap coefficient of a class for the data of interest. A prediction step of predicting data of interest from the prediction tap and supplying the data to the data processing device. Ram, characterized in that it has been properly recorded.

  According to a first data processing apparatus of the present invention, there is provided an attaching / detaching unit to which the first to Nth storage units are attached, and generating information in the first to N ′ (≦ N) storage units attached to the attaching / detaching unit. Tap coefficient generation control means for controlling the generation of tap coefficients from the memory, input / output route setting means for setting an input / output route of data to each of the first to N'th storage devices, and input / output route setting means. Data supply control means for controlling the supply of data from one storage device of the first to N 'storage devices to another storage device in accordance with the input / output route.

  A second data processing method according to the present invention includes a tap coefficient generation control step of controlling generation of tap coefficients from generation information in first to N ′ (≦ N) storage devices attached to the attaching / detaching means; An input / output route setting step for setting an input / output route of data to each of the first to N'th storage devices; and an input / output route set in the input / output route setting step. A data supply control step of controlling data supply from one of the storage devices to the other storage device.

  A second program according to the present invention includes a tap coefficient generation control step of controlling generation of tap coefficients from generation information in first to N ′ (≦ N) storage devices attached to the attaching / detaching means; An input / output route setting step of setting an input / output route of data to each of the storage devices of the first to N′th storage units; A data supply control step of controlling data supply from one storage device to another storage device.

  A second recording medium according to the present invention includes a tap coefficient generation control step of controlling generation of tap coefficients from generation information in first to N ′ (≦ N) storage devices attached to the attaching / detaching means; An input / output route setting step for setting an input / output route of data to each of the first to N'th storage devices; and an input / output route set in the input / output route setting step. A data supply control step of controlling data supply from one storage device to another storage device.

  In the first data processing system of the present invention, each of the first to Nth storage devices stores generation information according to control by the data processing device, and generation information storage means for storing generation information for generating tap coefficients. Extracting, from the first data supplied from the data processing device, a tap coefficient generating means for generating a tap coefficient, and a prediction tap used for predicting the noted data of interest among the second data. Prediction tap extracting means, and a class tap for extracting, from the first data supplied from the data processing device, a class tap used for performing class classification for classifying the data of interest into one of a plurality of classes Extraction means, class classification means for classifying the data of interest based on the class tap, and tap coefficient of the class for the data of interest , A prediction tap for predicting target data from the prediction tap and supplying the data to the data processing device, wherein the data processing device is attached to and detached from the first to Nth storage units, and attached to the detachment unit. Tap coefficient generation control means for controlling generation of tap coefficients from generation information in the first to N'th (≤N) storage devices, and a data input / output route to each of the first to N'th storage devices And data according to the input / output route set by the input / output route setting means, from one storage device of the first to N ′ storage devices to another storage device. Data supply control means for controlling the supply of data.

  The second data processing device according to the present invention is characterized in that the attaching / detaching unit to which the first to Nth storage units are attached and the information generated in the first to N'th (≦ N) storage units attached to the attaching / detaching unit. Tap coefficient generating means for generating tap coefficients; predictive tap extracting means for extracting first data to be used as predictive taps used for predicting data of interest of the second data; Class tap extracting means for extracting first data which is used as a class tap for classifying data into any one of a plurality of classes; and classifying the data of interest based on the class tap. And a prediction means for predicting the data of interest from the tap coefficient of the class for the data of interest and the prediction tap.

  According to a third data processing method of the present invention, there is provided a tap coefficient generating step of generating a tap coefficient from generation information in first to N ′ (≦ N) storage devices attached to a data processing device; A predictive tap extracting step of extracting first data to be used as a predictive tap used for predicting target data of interest among data; and a class for classifying the target data into one of a plurality of classes. A class tap extracting step of extracting first data as a class tap used for performing classification; a class classification step of classifying the data of interest based on the class tap; and a tap coefficient of a class for the data of interest. And a prediction tap for predicting the data of interest from the prediction tap.

  A third program according to the present invention includes a tap coefficient generation step of generating a tap coefficient from generation information in first to N ′ (≦ N) storage devices attached to a data processing device; A prediction tap extraction step of extracting first data as prediction taps used for predicting the data of interest, and a class classification of classifying the data of interest into one of a plurality of classes. A class tap extracting step of extracting first data to be used as a class tap to be performed, a class classifying step of classifying target data based on the class tap, a tap coefficient of a class for the target data, and a prediction A prediction step of predicting target data from a tap.

  A third recording medium according to the present invention includes a tap coefficient generation step of generating a tap coefficient from generation information in first to N ′ (≦ N) storage devices mounted on a data processing device; Prediction tap extracting step of extracting first data as prediction taps used for predicting the data of interest of interest, and class classification of classifying the data of interest into one of a plurality of classes A class tap extraction step of extracting first data as a class tap used to perform the class tap; a class classification step of classifying the data of interest based on the class tap; a tap coefficient of a class for the data of interest; A program including a prediction tap and a prediction step of predicting target data from the prediction tap is recorded.

  In the second data processing system of the present invention, each of the first to Nth storage devices is generated information, and a tap coefficient is obtained from the generated information and the generated information stored in another storage device. And a generation information supply unit for supplying the generation information to the data processing device, wherein the data processing device comprises: a detachable unit to which the first to Nth storage units are attached; A tap coefficient generation unit for generating a tap coefficient, based on the generation information in the first to N ′ (≦ N) storage devices attached to the unit, and a target data of interest among the second data is predicted Predictive tap extracting means for extracting first data to be used as predictive taps, and class taps for performing class classification for classifying target data into one of a plurality of classes. Class tap extracting means for extracting first data to be extracted, class classifying means for classifying the target data based on the class taps, predicting the target data from the tap coefficients of the class for the target data, and predicting taps And a prediction unit that performs the prediction.

  In the first storage device, the first data processing method, the first program, and the first recording medium of the present invention, a tap coefficient is generated from generation information according to control by the data processing device. Further, a prediction tap used for predicting the target data of interest in the second data, and a class used for performing class classification for classifying the target data into any of a plurality of classes Taps are extracted from the first data supplied from the data processing device. Further, the data of interest is classified based on the class tap, and the data of interest is predicted from the tap coefficient of the class for the data of interest and the prediction tap, and is supplied to the data processing device.

  In the first data processing device and the second data processing method, and the second program and the second recording medium of the present invention, the first to N ′ (≦ N) storage devices mounted on the attaching / detaching means , The generation of tap coefficients from the generation information is controlled, and the data input / output routes for the first to N'th storage devices are set. In accordance with the set input / output route, the supply of data from one storage device of the first to N ′ storage devices to another storage device is controlled.

  In the first data processing system of the present invention, a tap coefficient is generated from the generated information in each of the first to Nth storage devices according to control by the data processing device. Further, a prediction tap used for predicting the target data of interest in the second data, and a class used for performing class classification for classifying the target data into any of a plurality of classes Taps are extracted from the first data supplied from the data processing device. Further, the data of interest is classified based on the class tap, and the data of interest is predicted from the tap coefficient of the class for the data of interest and the prediction tap, and is supplied to the data processing device. On the other hand, in the data processing device, the generation of tap coefficients from the generation information in the first to N ′ (≦ N) storage devices attached thereto is controlled, and the data for each of the first to N ′ storage devices is controlled. Is set. In accordance with the set input / output route, the supply of data from one storage device of the first to N ′ storage devices to another storage device is controlled.

  In the second data processing device and the third data processing method, and the third program and the third recording medium of the present invention, the first to N ′ (≦ N) storages mounted on the data processing device A tap coefficient is generated from the generation information in the device. Further, first data as a prediction tap used for predicting attention data of interest out of the second data, and a class classification for classifying the attention data into one of a plurality of classes are provided. First data is extracted as a class tap used for performing. Further, the target data is classified into classes based on the class taps, and the target data is predicted from the tap coefficients of the class of the target data and the prediction taps.

  In the second data processing system of the present invention, in each of the first to Nth storage devices, a tap coefficient is obtained from the generated information and the generated information stored in another storage device. Is generated, and the generated information is supplied to the data processing device. On the other hand, in the data processing device, the tap coefficient is generated from the generation information in the first to N ′ (≦ N) storage devices attached thereto. Further, first data as a prediction tap used for predicting attention data of interest out of the second data, and a class classification for classifying the attention data into one of a plurality of classes are provided. First data is extracted as a class tap used for performing. Then, the target data is classified into classes based on the class taps, and the target data is predicted from the tap coefficients of the class of the target data and the prediction taps.

  According to the present invention, the functions of the device can be additionally enhanced.

  FIG. 1 shows an example of an external configuration of a television receiver according to an embodiment of the present invention.

  In the embodiment shown in FIG. 1, the television receiver includes a main body 1 and a remote controller (remote controller) 2.

  A CRT (Cathod Ray Tube) 11 is provided on the front of the main body 1, and displays an image such as a television broadcast program on the CRT 11.

Further, in the lower portion of the main body 1, ₁ to 12 ₆ six slots 12 are provided. The slot 12 _i is adapted to be able to attach and detach the image processing card 13 _j.

Incidentally, in the embodiment shown in FIG. 1, six in slot 12 ₁ of the slots 12 ₁ to 12 ₆ is as it were mounted default image processing card 13 ₁ is already further the slot 12 _1, there so can not be easily inserted and removed an image processing card 13 ₁ is mounted from the start, are covered with a cover.

Further, in the embodiment shown in FIG. 1, although six slots 12 ₁ to 12 ₆ are provided, the number of slots is not limited to six slots, be 5 slots less and 7 slots or It is possible.

The image processing card 13, in IC (Integrated Curcuit) card or a memory card that can continue to add functionality to the main body 1 of the television receiver, by attaching to the slot 12 _i, the user, as described below Provides various functions.

  Here, as the image processing card 13, for example, a card conforming to an existing standard such as PCMCIA (Personal Computer Memory Card International Association) can be adopted. However, it is also possible to adopt a unique standard as the image processing card 13.

  The remote controller 2 is operated when giving an instruction to change the reception channel and volume of the main body 1 and the like. The remote controller 2 emits an infrared ray corresponding to the operation, and the main body 1 receives the infrared ray and performs a process corresponding to the operation of the remote controller 2.

  In addition, as the remote controller 2, for example, a remote controller that emits a radio wave such as Bluetooth (trademark) other than infrared rays can be adopted.

  Next, FIG. 2 shows a rear surface of the main body 1 of FIG.

  An antenna terminal 21 to which an antenna (not shown) is connected, an input terminal 22 for inputting an image and a voice to the main body 1, and an output for outputting an image and a voice from the main body 1 are provided on the back of the main body 1. A terminal 23 is provided.

In the embodiments of Figure 1, slots 12 ₁ to 12 ₆ provided on the front of the body 1, as shown in FIG. 2, it is possible to be provided on the rear surface of the main body 1.

Further, the slot 12 ₁ to 12 _6, all of them, instead of providing on either of the front or rear of the main body 1, a part in front, the rest to the back, to be so provided, respectively It is possible.

Furthermore, in the present embodiment, the image processing card 13 _j is configured as a PCMCIA card. However, the image processing card 13 _j may be replaced by a PCI (Peripheral Component Interconnect) cards. In this case, the slot 12 _i needs to be configured as a connector to which a PCI card can be attached.

  Next, FIG. 3 shows an example of an electrical configuration of the main body 1 of FIG.

  A received signal received by an antenna (not shown) is supplied from the antenna terminal 21 to the tuner 31. The tuner 31 transmits a television broadcast signal of a predetermined channel under the control of the controller 37. The signal is detected and demodulated and supplied to an A / D (Analog / Digital) converter 32.

  The A / D converter 32 A / D converts the television broadcast signal from the tuner 31 and supplies the image data of the A / D conversion result to the Y / C separation unit 33.

  Here, the A / D converter 32 also outputs audio data of the television broadcast signal, and the audio data is supplied to a speaker (not shown) and output.

  The Y / C separation section 33 performs Y / C separation on the image data from the A / D converter 32 and supplies the image data to the selector 34. The selector 34 selects one of the image data supplied from the Y / C separation unit 33 and the image data supplied from the input terminal 22 under the control of the controller 37 and supplies the selected data to the frame memory 35. .

  The frame memory 35 stores the image data supplied from the selector 34 and supplies the image data to the output terminal 23 and the matrix conversion unit 36 as they are. Alternatively, the frame memory 35 stores the image data supplied from the selector 34, and supplies the stored image data to the image processing interface 40. Further, the frame memory 35 stores the image data subjected to the predetermined image processing supplied from the image processing interface 40, and supplies the image data to the output terminal 23 and the matrix conversion unit 36.

  The frame memory 35 has at least first to fourth banks capable of storing image data for one frame (or field), for example. In the frame memory 35, the image data from the selector 34 is alternately written to the first and second banks, and the image data from the selector 34 of the first and second banks is written. The image data is read from the bank in which is not performed, and is supplied to the image processing interface 40. Further, in the frame memory 35, the image data output from the image processing interface 40 is alternately written to the third and fourth banks, and the image data from the image processing interface 40 among the third and fourth banks is written. The image data is read from the bank to which the image data has not been written, and is supplied to the matrix conversion unit 36.

  The frame memory 35 stores the image data supplied from the selector 34, reads out the image data to the image processing interface 40, and stores the image data from the image processing interface 40 by performing the bank switching as described above. Writing and reading of image data to the matrix conversion unit 36 can be performed in real time.

  The matrix conversion unit 36 converts the image data supplied from the frame memory 35 into RGB (Red, Green, Blue) image data, performs D / A conversion, and outputs the result. The image data output from the matrix conversion unit 36 is supplied to the CRT 11 and displayed.

  The controller 37 includes a CPU (Central Processing Unit) 37A, an EEPROM (Electrically Erasable Programmable Read Only Memory) 37B, a RAM (Random Access Memory) 37C, and the like. The tuner 31, the selector 34, the communication interface 38, and the image processing interface 40 and the like are controlled.

  That is, various processes are executed in accordance with programs stored in the CPU 37A and the EEPROM 37B, thereby controlling, for example, the tuner 31, the selector 34, the communication interface 38, and the image processing interface 40. Further, the CPU 37A executes a process corresponding to the command supplied from the IR (Infrared Ray) interface 39. Further, by controlling the communication interface 38, the CPU 37A accesses a server (not shown) via a telephone line or the like, and acquires an upgraded program and necessary data.

  The EEPROM 37B stores data and programs to be retained even after the power is turned off. The data and programs stored in the EEPROM 37B can be upgraded by overwriting the data and programs.

  The RAM 37C temporarily stores data and programs necessary for the operation of the CPU 37A.

  The communication interface 38 includes, for example, an analog modem, an ADSL (Asymmetric Digital Subscriber Line) modem, a DSU (Digital Service Unit) and a TA (Terminal Adapter), a LAN (Local Area Network) card, and the like. Below, communication control via a telephone line or other communication lines is performed.

  The IR interface 39 receives the infrared rays from the remote controller 2, performs photoelectric conversion, and supplies a corresponding electric signal to the controller 37.

The image processing interface 40, image processing card 13 ₁ to 13 ₆ has a slot 12 ₁ to 12 ₆ to be mounted, together with the attached image processing card 13 _j in the slot 12 _i, stored in the frame memory 35 Image processing (data conversion processing) to be described later is performed on the processed image data.

In the embodiment of FIG. 3, the image processing card 13 _i is, but is adapted to be mounted in the slot 12 _i, the image processing card 13 _i is any of the six slots 12 ₁ to 12 ₆ Slot. However, in the present embodiment, it is assumed that the image processing card 13 _i is installed in the slot 12 _i for simplicity of description.

Further, in the present embodiment, the image processing interface 40, six slots 12 ₁ to 12 ₆ is provided, therefore, to be able to mount the 6 types of image processing card 13 ₁ through 13 ₆ Has become. In the embodiment of FIG. 3, the slot 12 ₁ to 12 _3, three types of image processing card 13 ₁ through 13 ₃ are respectively mounted.

Further, in the present embodiment, a predetermined terminal is provided inside the slot 12 _i , and when the terminal and the image processing card 13 _i are in physical contact, the image processing card 13 _i The image processing interface 40 is electrically connected, and various data is exchanged between them. However, data exchange between the image processing card 13 _i and the image processing interface 40 can be performed by wireless communication.

  Next, FIG. 4 is a plan view illustrating a configuration example of the remote controller 2.

  The select button switch 51 can be operated (directionally operated) in a total of eight directions of four oblique directions in addition to four directions of up, down, left, and right directions. Further, the select button switch 51 can be pressed (selected) also in a direction perpendicular to the upper surface of the remote controller 2. The menu button switch 54 is operated when the CRT 11 of the main body 1 displays a menu screen for inputting various settings and a command for performing a predetermined process.

  Here, when the menu screen is displayed, a cursor indicating an item or the like on the menu screen is displayed on the CRT 11. The cursor moves in a direction corresponding to the operation by operating the select button switch 51 in the direction. When the select button switch 51 is operated while the cursor is at a position on a predetermined item, the selection of the item is determined. It should be noted that icons can be displayed on the menu, and the select button switch 51 is also selected when the icon is clicked.

  The exit button switch 55 is operated, for example, when returning from the menu screen to the original normal screen.

  The volume button switch 52 is operated when raising or lowering the volume. The channel up / down button switch 53 is operated when the number of the broadcast channel to be received is up or down.

  A numeric button (numerical key) switch 58 on which numbers 0 to 9 are displayed is operated when the displayed numbers are input. When the operation of the numeric button switch 58 is completed, the enter button switch 57 is operated subsequently to indicate the end of numeric input. When the channel is switched, a new channel number or the like is displayed on the CRT 11 of the main body 1 for a predetermined time by OSD (On Screen Display). The display button 56 is operated to turn on / off the OSD display such as the number of the currently selected channel and the current volume.

  The TV / video switching button switch 59 is operated when switching the input of the main body 1 to the input from the tuner 31 or the input terminal 22. The TV / DSS switch button switch 60 is operated when the tuner 31 selects a television mode for receiving terrestrial broadcasts or a DSS (Digital Satellite System (trademark of Hughes Communications)) mode for receiving satellite broadcasts. . When the channel is switched by operating the numeric button switch 58, the channel before the switching is stored, and the jump button switch 61 is operated when returning to the original channel before the switching.

  The language button 62 is operated to select a predetermined language when broadcasting is performed in two or more languages. The guide button switch 63 is operated when displaying an EPG (Electric Program Guide) on the CRT 11. The favorite button switch 64 is operated when a user's favorite channel set in advance is selected.

  The cable button switch 65, the television switch 66, and the DSS button switch 67 are button switches for switching a device category of a command code corresponding to infrared rays emitted from the remote controller 2. That is, the remote controller 2 can remotely control the STB and IRD (not shown) in addition to the main body 1 of the television receiver, and the cable button switch 65 is transmitted via a CATV network. This is operated when the remote controller 2 controls the STB (Set Top Box) that receives the signal. After the operation of the cable button switch 65, the remote controller 2 emits infrared light corresponding to the command code of the device category assigned to the STB. Similarly, the TV button switch 66 is operated when the main body 1 is controlled by the remote controller 2. The DSS button switch 67 is operated when the remote controller 2 controls an IRD (Integrated Receiver and Decorder) that receives a signal transmitted via a satellite.

  The LED (Light Emitting Diode) 68, 69, 70 lights up when the cable button switch 65, the television button switch 66, or the DSS button switch 67 is turned on, respectively, whereby the remote controller 2 The user is informed whether control of the apparatus is enabled. The LEDs 68, 69, and 70 are turned off when the cable button switch 65, the television button switch 66, or the DSS button switch 67 is turned off.

  The cable power button switch 71, the television power button switch 72, and the DSS power button switch 73 are operated when turning on / off the power of the STB, the main body 1, or the IRD.

  The muting button switch 74 is operated when setting or canceling the muting state of the main body 1. The sleep button switch 75 is operated to set or cancel the sleep mode in which the power is automatically turned off at a predetermined time or when a predetermined time has elapsed.

  When the remote controller 2 is operated, the light emitting unit 76 emits infrared rays corresponding to the operation.

  Next, FIG. 5 shows a configuration example of the first embodiment of the image interface 40 of FIG.

The interface controller 81 controls the memory interface 82. Further, the interface controller 81 recognizes the attachment / detachment of the image processing card 13 _i to / from the slot 12 _i based on the output of the connection detecting section 84, and controls the card interface 83 based on the recognition result. Further, the interface controller 81 controls exchange of image data between the memory interface 82 and the card interface 82.

  The memory interface 82 reads out image data from the frame memory 35 (FIG. 3) under the control of the interface controller 81 and supplies the image data to the interface controller 81. The memory interface 82 receives the image data supplied from the interface controller 81 and supplies the image data to the line-sequential conversion unit 85.

The card interface 83 supplies the image data supplied from the interface controller 81 to the image processing card 13 _i mounted on the slot 12 _{i under} the control of the interface controller 81.

That is, the card interface 83 is connected to the slot 12 ₁ to 12 _6, the interface controller 81 receives the image data and control signals other data supplied from the slot 12 _i to the mounting image processing card 13 _i To supply.

Moreover, the card interface 83 receives the image data and control signals other data supplied from the slot 12 _i to the mounting image processing card 13 _i, is attached to the interface controller 81 or other slot 12 _j, other image processing cards 13 supplied to the _j of.

Further, the card interface 83 supplies a voltage (terminal voltage) of a terminal (not shown) of the slot 12 _i to the connection detecting unit 84.

Connection detection unit 84 via the card interface 83, for example, monitors the terminal voltage of the slots 12 ₁ to 12 _6, based on the change in the terminal voltage, the mounting of the image processing cards 13 _i for the slots 12 _i Alternatively, the extraction is detected and the detection result is supplied to the interface controller 83. The detection of detachment of the image processing cards 13 _i is other can also be done mechanically possible.

  The line-sequential conversion unit 85 changes the scanning method of the image data supplied from the memory interface 82 from an interlaced scanning method to a progressive scanning (non-interlaced scanning) method, or from a progressive scanning method to an interlaced scanning method, as necessary. And supplies it to the frame memory 35 for storage.

Next, FIG. 6, the image processing interface 40 shows a configuration example of the image processing cards 13 _i when configured as shown in FIG.

In the embodiment of FIG. 6, the image processing card _13i uses the image data supplied thereto as first image data, and converts the first image data into a higher-quality image than the first image data. A data conversion process for converting the image data into the second image data is performed.

  Here, for example, if the first image data is low-resolution image data and the second image data is high-resolution image data, the data conversion process is referred to as a resolution improvement process for improving the resolution. it can. Further, for example, if the first image data is low S / N (Siginal / Noise) image data and the second image data is high S / N image data, the data conversion process can reduce noise. This can be referred to as noise removal processing. Further, for example, if the first image data is image data of a predetermined size and the second image data is image data in which the size of the first image data is increased or decreased, the data conversion process is performed as follows. This can be referred to as resizing processing for resizing (enlarging or reducing) an image.

  Therefore, according to the data conversion process, various processes can be realized depending on how the first and second image data are defined.

  First image data to be subjected to data conversion processing is supplied to the tap extracting units 91 and 92 from the card interface 83 (FIG. 5).

  The tap extraction unit 91 sequentially sets pixels constituting the second image data as a target pixel, and further, (pixel values of pixels constituting the first image data used for predicting a pixel value of the target pixel) ) Are extracted as prediction taps.

  Specifically, the tap extracting unit 91 determines the pixel of the first image data corresponding to the target pixel (for example, from the same position as the position of the target pixel on the first image data, for example, A plurality of pixels (for example, a pixel of the first image data corresponding to the pixel of interest and a pixel spatially adjacent to the pixel of the first image data) which are spatially or temporally close to the pixel of the image data of FIG. Is extracted as a prediction tap.

  The tap extracting unit 92 extracts, as class taps, some of the pixels constituting the first image data used for performing the class classification for classifying the target pixel into one of several classes.

  Note that a control signal is supplied from the card controller 98 to the tap extracting units 91 and 92, and the tap structure of the prediction tap configured by the tap extracting unit 91 and the tap extracting unit 92 The tap structure of the class tap to be set is set in accordance with a control signal from the card controller 98.

  Here, the prediction tap and the class tap can have the same tap structure or different tap structures.

  The prediction tap obtained by the tap extraction unit 91 is supplied to a prediction unit 95, and the class tap obtained by the tap extraction unit 92 is supplied to a class classification unit 93.

  The class classification unit 93 classifies the pixel of interest based on the class tap from the tap extraction unit 92, and supplies a class code corresponding to the resulting class to the coefficient memory 94.

  Here, as a method of performing the class classification, for example, ADRC (Adaptive Dynamic Range Coding) or the like can be adopted.

  In the method using ADRC, a pixel value of a pixel forming a class tap is subjected to ADRC processing, and a class of a pixel of interest is determined according to a resulting ADRC code.

In the K-bit ADRC, for example, the maximum value MAX and the minimum value MIN of the pixel values of the pixels constituting the class tap are detected, and DR = MAX-MIN is set as a local dynamic range of the set. Based on the DR, the pixel values forming the class tap are requantized to K bits. That is, the pixel value of each pixel forming the class taps, the minimum value MIN is subtracted, and the subtracted value is divided (quantized) by DR / 2 ^K. Then, a bit string obtained by arranging the pixel values of the K-bit pixels constituting the class tap in a predetermined order is output as an ADRC code. Therefore, when the class tap is subjected to, for example, 1-bit ADRC processing, the pixel value of each pixel forming the class tap is obtained by subtracting the minimum value MIN from the average value of the maximum value MAX and the minimum value MIN. The pixel value of each pixel is set to 1 bit (binarized). Then, a bit string in which the one-bit pixel values are arranged in a predetermined order is output as an ADRC code.

The class classification unit 93 can directly output, for example, the pattern of the level distribution of the pixel values of the pixels constituting the class tap as the class code. However, in this case, assuming that the class tap is composed of the pixel values of N pixels and K bits are assigned to the pixel value of each pixel, the number of class codes output by the classifying unit 93 is as follows. Becomes (2 ^N ) ^K , which is a huge number exponentially proportional to the number K of bits of the pixel value of the pixel.

  Therefore, it is preferable that the class classification unit 93 performs the class classification by compressing the information amount of the class tap by the above-described ADRC processing or vector quantization.

  Note that, as described above, the class classification is performed based on the pattern of the level distribution of the pixel values of the pixels forming the class tap. For example, an edge exists in a pixel portion corresponding to the target pixel of the class tap. It can also be performed based on whether or not the pixel has moved (or the magnitude or direction of the movement).

  The coefficient memory 94 stores the tap coefficients for each class supplied from the coefficient generation unit 96 via the coefficient normalization unit 99, and further supplies the tap coefficients from the class classification unit 93 among the stored tap coefficients. The tap coefficient (the tap coefficient of the class represented by the class code supplied from the class classification unit 93) stored at the address corresponding to the class code is supplied to the prediction unit 95.

  Here, the tap coefficient corresponds to a coefficient multiplied by input data in a so-called tap in a digital filter.

  The prediction unit 95 acquires the prediction tap output by the tap extraction unit 91 and the tap coefficient output by the coefficient memory 94, and obtains a predicted value of the true value of the pixel of interest using the prediction tap and the tap coefficient. A predetermined prediction operation is performed. Thereby, the prediction unit 95 obtains and outputs the pixel value (predicted value) of the pixel of interest, that is, the pixel value of the pixel configuring the second image data.

  The coefficient generation unit 96 generates tap coefficients for each class based on the generation information stored in the generation information storage unit 97 and the parameters supplied from the card controller 98, and outputs the generated tap coefficients via the coefficient normalization unit 99. Is supplied to the coefficient memory 94 and stored in an overwritten form.

  The generation information storage unit 97 stores generation information for generating a tap coefficient for each class.

  Note that, as the generation information, for example, the coefficient seed data itself, which is a so-called seed of the tap coefficient, which is obtained by learning described later, or information capable of generating the coefficient seed data can be adopted.

The image processing card 13 ₁ through 13 ₆ are mounted in slot 12 ₁ to 12 ₆ each of the image processing interface 40, is constructed as illustrated in all 6, is stored in the generation information storage section 97 The generation information is such that different tap coefficients can be generated between different image processing cards 13.

The generation information storage unit 97 further stores processing information indicating the contents of data conversion processing as image processing performed by the image processing card _13i .

  The processing information includes information indicating the tap structure of the prediction tap generated (configured) by the tap extraction unit 91 and the class tap generated by the tap extraction unit 92, and the method of class classification performed by the class classification unit 93. (E.g., information such as performing class classification based on a pattern of pixel value level distribution, presence / absence of an edge, presence / absence of a motion of a pixel, etc.), and a tap coefficient generated from generation information And the number of classes.

Further, the processing information includes the image processing cards 13 _i of the card ID (for the Identification) is. Here, the card ID, the image processing cards 13 _i, so to speak but put order, in this embodiment, the image processing card 13 ₁ through 13 ₆ are mounted on six slots 12 ₁ to 12 _6, respectively For example, card IDs 1 to 6 are respectively assigned.

Then, except for the image processing card 13 ₁ of # 1 card ID is the highest rank, the image processing cards 13 _i of the card ID is #i, it than one order of large card ID # i-1 of the image processing If the card 13 _i-1 does not exist, it does not operate.

Therefore, in order to card ID to operate the image processing cards 13 ₆ # 6, the card ID is required the presence of the image processing cards 13 ₅ # 5, the image processing cards 13 of the card ID is # 5 to ₅ operates, the card ID is required the presence of the image processing cards 13 ₄ # 4. In the same manner, after all, the card ID is To operate the image processing cards 13 ₆ # 6, from the rank it is necessary that all of the image processing cards 13 ₁ through 13 ₅ above.

In the present embodiment, the processing information of the image processing card 13 _{i having} the card ID #i includes, in addition to the processing information indicating the content of the data conversion processing performed by the image processing card 13 _i itself, the image processing card 13 _{i When} all the image processing cards 13 _i-1 , 13 _i-2 ,..., 13 ₁ having higher ranks are attached together with _i, each of the image processing cards 13 _i-1 , 13 _{i- 2,} ..., it is also included processing information indicating the contents of the data conversion processing 13 ₁ performs.

That is, for example, if the image processing card 13 ₁ of the card ID # 1 as an example, in this embodiment, the image processing card 13 _1, and if only is attached thereto, the image processing cards 13 ₁ and 13 ₂ The function (contents of the data conversion process) can be changed between when and is mounted. Therefore, when only the image processing card 13 ₁ is attached, the content of the data conversion process of the image processing cards 13 ₁ is defined in the image processing card 13 ₁ of processing information, the image processing card 13 ₁ and when both of 13 ₂ has been mounted, the content of the data conversion process of the image processing cards 13 ₁ is defined in the image processing card 13 ₂ of processing information. Incidentally, in the case where both of the image processing cards 13 ₁ and 13 ₂ are mounted, the contents of the data conversion process of the image processing cards 13 ₂ is defined in the image processing card 13 ₂ of processing information.

Similarly, in the case where the image processing card 131 _to 13 _i is mounted, the contents of the image processing cards 13 ₁ through 13 _i each data conversion process, (the least order) most card ID is greater image processing card _13i is defined in the processing information.

Thus, in this embodiment, when the image processing card 131 _to 13 _i is mounted, the content of the data conversion processing of the image processing card 131 _to 13 _i is most card ID is greater image processing card 13 _i Can be recognized by referring to the processing information.

  The card controller 98 controls exchange of image data and other data with the card interface 83 (FIG. 5). Further, the card controller 98 controls the tap extraction units 91 and 92 and the class classification unit 93. Further, the card controller 98 reads processing information from the generation information storage unit 97 in response to a request from the image processing interface 40 and supplies the processing information to the image processing interface 40. Further, the card controller 98 gives a parameter to be described later to the coefficient generator 96.

  The coefficient normalizing unit 99 normalizes the tap coefficients supplied from the coefficient generating unit 96, and supplies the normalized tap coefficients to the coefficient memory 94 for storage.

  Next, the processing of the image processing interface 40 of FIG. 5 will be described with reference to the flowchart of FIG.

In the image processing interface 40, first, at step S1, the interface controller 81, based on the output of the connection detecting portion 84, with respect to one of the slots 12 _i of the slots 12 ₁ to 12 _6, the image processing card It is determined whether _13i is newly mounted (inserted).

In step S1, if the relative slot 12 _i, the image processing card 13 _i is determined to have been attached, i.e., an image processing card 13 _i is, by being mounted in the slot 12 _i, the terminal of the slot 12 _i When the voltage changes and the change is detected by the connection detecting unit 84, the process skips step S2 and proceeds to step S3.

If it is determined in step S1 that the image processing card 13 _i is not mounted in the slot 12 _i , the process proceeds to step S 2, where the interface controller 81 determines the slot 12 _i based on the output of the connection detection unit 84. It determines whether the image processing cards 13 _i that is mounted in one of the slots 12 _i of ₁ to 12 ₆ has been removed.

In step S2, from the slot 12 _i, when the image processing card 13 _i is determined to not withdrawn, the flow returns to step S1, and similar processing is repeated.

Further, in step S2, if the slot 12 _i, the image processing card 13 _i is determined to have been removed, i.e., image processing card 13 _i is, by being withdrawn from the slot 12 _i, the terminal voltage of the slot 12 _i If but changed, the change was detected in the connection detection unit 84, the process proceeds to step S3, interlace controller 81, by controlling the card interface 83, of the slots 12 ₁ to 12 _6, the image processing card Although the image processing card 13 is mounted, processing information is read from all the mounted image processing cards 13 (hereinafter, appropriately referred to as mounted cards).

That is, in this case, the control signal requesting the processing information from the card interface 83, via a slot 12 _i, supplied thereto to the mounted image processing card 13 _i of the card controller 98 (FIG. 6). When receiving the control signal requesting the processing information, the card controller 98 reads the processing information from the generation information storage unit 79 and supplies the processing information to the card interface 83. The card interface 83 receives the processing information supplied from the image processing card 13 _i in this way and supplies the processing information to the interface controller 81.

  Thereafter, the process proceeds to step S4, where the interface controller 81 examines the card ID included in the processing information read from the image processing card 13 as the mounting card, and thereby, the mounting card to be operated (hereinafter, referred to as an effective card as appropriate) To determine.

That is, the interface controller 81 arranges all card IDs of the image processing card 13 as mounting cards in ascending order, and when card IDs from 1 to n are consecutive, the card IDs are 1 to n respectively. The image processing cards (mounting cards) 13 _{1 to} 13 _n are valid cards.

Thus, each of the plurality of slots of the slots 12 ₁ to 12 _6, even the image processing card 13 has been mounted, when the image processing card 13 ₁ of the card ID # 1 is not mounted, either The image processing card 13 is also not a valid card. Further, for example, if the image processing cards 13 ₁ and the card ID # image processing card 13 ₃ of the third card ID # 1 is be mounted, the image processing card 13 ₂ of the card ID # 2 is not attached , being valid card is only the image processing card 13 ₁ of the card ID # 1, the image processing cards 13 ₃ of the card ID # 3 is not the valid card.

Here, the plurality of the slots 12 ₁ to 12 _6, when the image processing card 13 having the same card ID is attached, the interface controller 81, for example, the image processing card 13 of the same card ID One of them is selected as a valid card.

  As described above, when a plurality of image processing cards 13 having the same card ID are mounted, or when image processing cards having discontinuous card IDs are mounted as described above, the interface controller The controller 81 supplies, for example, a control signal indicating this to the controller 37. In this case, the controller 37 causes the CRT 11 to display a predetermined message via the selector 34, the frame memory 35, and the matrix conversion unit 36, whereby a plurality of image processing cards 13 having the same card ID are mounted. To the user or that an image processing card with a non-consecutive card ID is mounted.

Interface controller 81, in step S4, as described above, when determining the effective card, the process proceeds to step S5, by controlling the card interface 83, the card controller 98 (Fig each image processing card 13 _i as an effective card In response to 6), the processing information read out from the image processing card 13 _{i (max)} having the largest card ID among the image processing cards 13 _i as valid cards (hereinafter, appropriately referred to as maximum ID processing information) is transmitted. Thereby, the tap structure of the prediction tap and the class tap in each image processing card 13 _i which is an effective card, the class classification method, the generation of the tap coefficient, and the like are controlled.

Here, each image processing card 13 _i as an effective card recognizes the contents of the data conversion processing to be performed by itself by referring to the maximum ID processing information supplied from (the interface controller 81 of) the image processing interface 40. Thus, the prediction tap and the class tap having the tap structure according to the maximum ID processing information are generated, the class is classified by a predetermined class classification method, and the predetermined tap coefficient is generated.

  Thereafter, in step S6, the interface controller 81 determines an input / output route of the image data with respect to the image processing card 13 which is a valid card, and transfers the image data according to the input / output route. 83 is set, and the process returns to step S1.

That is, the interface controller 81 determines the input / output route of the image processing card 13 which is the valid card so that the image data read from the frame memory 35 is transferred in the order of the card ID. Thus, for example, all slots 12 ₁ to 12 ₆ connected to the card interface 83, when the image processing card 13 ₁ through 13 ₆ of the card ID # 1 to # 6 are respectively mounted to an image An input / output route is determined so that image data is transferred in the order of the processing cards 13 ₁ , 13 ₂ , 13 ₃ , 13 ₄ , 13 ₅ , and 13 ₆ .

After that, the image data is transferred in the card interface 83 according to the determined input / output route. Therefore, for example, as described above, when an input / output route is determined to transfer image data in the order of the image processing cards 13 ₁ , 13 ₂ , 13 ₃ , 13 ₄ , 13 ₅ , and 13 ₆ , card interface 83, the memory interface 82 is read from the frame memory 35, the image data supplied via the interface controller 81 is transferred to the image processing card 13 _1. The card interface 83 is an image processing card 13 _1, the data conversion process for the image data and receipt image data after the data conversion process, the image data is transferred to the image processing card 13 _2. Hereinafter, similarly, the card interface 83 transfers the image data in the order of the image processing cards 13 ₃ , 13 ₄ , 13 ₅ , and 13 ₆ , and upon receiving the image data from the last image processing card 13 ₆ , The image data is supplied to the interface controller 81. The image data supplied to the interlace controller 81 is stored in the frame memory 35 via the memory interlace 82 and the line-sequential conversion unit 85, and then displayed on the CRT 11.

  As described above, since the image data is transferred only to the image processing card 13 which is set as the valid card, the image data is not processed by the image processing card 13 which is not set as the valid card (the image data is processed because the image data is not supplied). Can't do that).

Next, the processing (data conversion processing) of the image processing card _{13i in} FIG. 6 will be described with reference to the flowchart in FIG.

  First, in step S11, the card controller 98 determines whether or not the processing information transmitted by the interface controller 81 (FIG. 5) in step S5 of FIG. 7 described above has been received.

Here, the processing information transmitted by the interface controller 81 (FIG. 5) in step S5 of FIG. 7 is the maximum ID processing information, and therefore, the data to be performed by each of the image processing cards 13 _i that are valid cards It has the contents of the conversion process.

In step S11, when it is determined that it has received the processing information, i.e., in any of the slots 12 ₁ to 12 _6, a new, or an image processing card 13 is mounted, or, slots 12 ₁ through 12 _{One of} the image processing cards attached to ₆ is extracted, whereby the image processing card 13 having the maximum card ID changes, and the image processing card 13 having the maximum card ID is stored. When the processing information (maximum ID processing information) is transmitted from (the card interface 83 of) the image processing interface 40 to the card controller 98 and received by the card controller 98, the process proceeds to step S12, and the card controller 98 determines the maximum ID. By referring to the processing information, it recognizes the contents of the data conversion processing to be performed by itself and recognizes it. In accordance with the result, the tap extracting unit 91 and 92, controls the classification unit 93.

  That is, the card controller 98 sets the operation mode of the tap extracting unit 91 or 92 so as to constitute, for example, a prediction tap or a class tap having a tap structure described in the maximum ID processing information. Further, the card controller 98 sets the operation mode of the class classification unit 93 so as to perform the class classification according to the class classification method described in the maximum ID processing information.

  Then, proceeding to step S13, the card controller 98 controls the coefficient generation unit 96 based on the maximum ID processing information to generate a tap coefficient from the generation information.

  That is, the processing information includes information on tap coefficients to be generated from the generation information (hereinafter, appropriately referred to as tap coefficient information), and the card controller 98 determines the tap coefficient from the generation information according to the tap coefficient information. The coefficient generator 96 is controlled so as to generate coefficients. Accordingly, the coefficient generation unit 96 generates tap coefficients for each class from the generation information stored in the generation information storage unit 97 and supplies the tap coefficients to the coefficient normalization unit 99, under the control of the card controller 98. At this time, the card controller 98 supplies a parameter, which will be described later, which is necessary for the coefficient generation unit 96 to generate a tap coefficient from the generation information, to the coefficient generation unit 96.

  When a tap coefficient for each class is generated in the coefficient generation unit 96 and supplied to the coefficient normalization unit 99, the coefficient normalization unit 99 determines the tap coefficient for each class in step S14 for level adjustment described later. , And the normalized tap coefficients for each class are supplied to and stored in the coefficient memory 94, and the process proceeds to step S15.

  In step S15, the tap extraction unit 91 converts the image data supplied from the card interface 83 (FIG. 5) according to the input / output route set in step S6 in FIG. The pixels constituting the second image data (the image data after the data conversion processing) for the first image data are sequentially set as a target pixel, and the target pixel has the tap structure set in step S12. A pixel of the first image data to be used as a prediction tap is extracted. Further, in step S15, the tap extracting unit 92 extracts, from the target pixel, pixels of the first image data that are to be class taps having the tap structure set in step S12. Then, the prediction tap is supplied from the tap extraction unit 91 to the prediction unit 95, and the class tap is supplied from the tap extraction unit 92 to the class classification unit 93.

  The class classification unit 93 receives the class tap for the pixel of interest from the tap extraction unit 92, and in step S16, classifies the pixel of interest based on the class tap by the class classification method set in step S12. Further, the class classification unit 93 outputs a class code representing the class of the pixel of interest obtained as a result of the classification to the coefficient memory 94, and proceeds to step S17.

  In step S17, the coefficient memory 94 reads and outputs the tap coefficient stored at the address corresponding to the class code supplied from the class classification unit 93, that is, the tap coefficient of the class corresponding to the class code. Further, in step S17, the prediction unit 95 acquires the tap coefficient output from the coefficient memory 94, and proceeds to step S18.

  In step S <b> 18, the prediction unit 95 performs a predetermined prediction operation (a prediction operation of Equation (1) described later) using the prediction tap output from the tap extraction unit 91 and the tap coefficient acquired from the coefficient memory 94. . Thereby, the prediction unit 95 obtains the pixel value of the target pixel and supplies the pixel value to the card interface 83 (FIG. 5). Then, the process returns to step S11, and thereafter, the same processing is repeated.

  On the other hand, if it is determined in step S11 that the processing information has not been received, steps S12 to S14 are skipped, the process proceeds to step S15, and the same processing as described above is performed. Therefore, in this case, the processing of steps S15 to S18 is performed in accordance with the processing information (maximum ID processing information) previously received by the card controller 98.

  Next, prediction calculation in the prediction unit 95 in FIG. 6, generation of tap coefficients in the coefficient generation unit 96, and learning of coefficient seed data, which is one of the generation information stored in the generation information storage unit 97, will be described.

  Now, high-quality image data (high-quality image data) is used as the second image data, and the high-quality image data is filtered by an LPF (Low Pass Filter) to lower the image quality (resolution). Using low-quality image data (low-quality image data) as first image data, a prediction tap is extracted from the low-quality image data, and the pixel value of the high-quality pixel is calculated using the prediction tap and a predetermined tap coefficient. It is considered to obtain (predict) by a predetermined prediction operation.

  Now, assuming that, for example, a linear primary prediction operation is adopted as the predetermined prediction operation, the pixel value y of the high-quality pixel is obtained by the following linear linear expression.

・・・（１）

... (1)

However, in the formula (1), x _n is forming the prediction taps for the high-quality pixel y, the pixel of the n-th low-quality image data (hereinafter referred to as low as the image quality pixel) represents the pixel values of, w _n represents an n-th tap coefficient multiplied by (the pixel value of) the n-th low-quality pixel.

In equation (1), the prediction tap is configured by _N low-quality pixels x ₁ , x ₂ ,..., X _N. In this case, according to Equation (1), the high-quality pixel y is obtained by converting N low-quality pixels x ₁ , x ₂ ,..., X _N into N tap coefficients w ₁ , w ₂ ,. , W _N as weights and weighted addition. Therefore, the tap coefficients w ₁ , w ₂ ,..., W _N , which are weights, need to have a total sum of 1 in order to prevent the level change of the high-quality pixel y obtained by the equation (1). . Therefore, the coefficient normalizing unit 99 (FIG. 6) of the image processing card 13 calculates the sum of the tap coefficients for each class with respect to the tap coefficients supplied from the coefficient generating unit 96, and, based on the sum, taps of the corresponding class. By dividing the coefficient, the tap coefficient is normalized.

  Here, the pixel value y of the high image quality pixel can be obtained not by the linear linear expression shown in Expression (1) but by a higher-order expression of second order or higher.

By the way, if the true value of the pixel value of the high-quality pixel of the k-th sample is represented by y _k and the predicted value of the true value y _k obtained by the equation (1) is represented by y _k ′, the prediction error _ek is represented by the following equation.

・・・（２）

... (2)

Now, since the predicted value y _k ′ in equation (2) is obtained according to equation (1), the following equation is obtained by replacing y _k ′ in equation (2) according to equation (1).

・・・（３）

... (3)

However, in equation (3), x _{n, k} represents the n-th low-quality pixel that constitutes a prediction tap for the high-quality pixel of the k-th sample.

Tap coefficient w _n to zero prediction error e _k of the formula (3) is, is the optimal for predicting the high-quality pixel, for all high quality pixels, determine such tap coefficients w _n That is generally difficult.

Therefore, _assuming that, for example, the least square method is adopted as a criterion indicating that the tap coefficient w _n is optimal, the optimal tap coefficient w _n is obtained by calculating the sum E of square errors represented by the following equation. It can be obtained by minimizing it.

・・・（４）

... (4)

However, in equation (4), K is a high-quality pixel y _k and low-quality pixels x _{1, k} , x _{2, k} ,..., X _N constituting a prediction tap for the high-quality pixel y _{k. , k} (the number of learning samples).

The tap coefficient w _n for minimizing (minimizing) the sum E of the square errors in the equation (4) is obtained by partially differentiating the sum E with the tap coefficient w _n to be 0, and thus satisfies the following equation. There is a need.

・・・（５）

... (5)

Therefore, the following equation is obtained by partially differentiating the above equation (3) with the tap coefficient w _n .

・・・（６）

... (6)

  From equations (5) and (6), the following equation is obtained.

・・・（７）

... (7)

By substituting equation (3) for e _k in equation (7), equation (7) can be represented by a normal equation shown in equation (8).

・・・（８）

... (8)

The normal equation of Expression (8) can be solved for the tap coefficient w _n by using, for example, a sweeping-out method (Gauss-Jordan elimination method).

By solving the normal equations in equation (8) for each class, the optimal tap coefficient (here, the tap coefficient that minimizes the sum E of square errors) to w _n, can be found for each class.

Figure 9 shows an example of the configuration of the learning device of the tap coefficients to perform the learning for determining the tap coefficient w _n for each class by solving the normal equations in equation (8).

  Learning image data used for learning is input to the learning device. Here, the learning image data corresponds to the second image data. For example, high-quality image data with high resolution can be used.

  In the learning device, the learning image data is supplied to the teacher data generation unit 111 and the student data generation unit 113.

  The teacher data generation unit 111 generates teacher data from the learning image data supplied thereto and supplies the generated teacher data to the teacher data storage unit 112. That is, here, the teacher data generation unit 111 supplies the high-quality image data as the learning image data to the teacher data storage unit 112 as, for example, the teacher data as it is.

  The teacher data storage unit 112 stores high-quality image data as teacher data supplied from the teacher data generation unit 111.

  The student data generation unit 113 generates student data corresponding to the first image data from the learning image data, and supplies the generated student data to the student data storage unit 114. That is, the student data generation unit 113 generates low-quality image data by, for example, filtering high-quality image data as learning image data, thereby reducing the resolution, and generating the low-quality image data. The data is supplied to the student data storage unit 114 as student data.

  The student data storage unit 114 stores the student data supplied from the student data generation unit 113.

  The tap extraction unit 115 sequentially sets pixels constituting high-quality image data as teacher data stored in the teacher data storage unit 112 as attention teacher pixels, and stores the attention teacher pixels in the student data storage unit 114. By extracting a predetermined one of the low-quality pixels constituting the low-quality image data as the student data, a prediction tap having the same tap structure as that configured by the tap extraction unit 91 in FIG. 6 is configured, It is supplied to the adding section 118.

  The tap extracting unit 116 extracts a predetermined one of the low-quality pixels constituting the low-quality image data as the student data stored in the student data storage unit 114 for the attention teacher pixel, thereby forming the tap in FIG. A class tap having the same tap structure as that configured by the extraction unit 92 is configured and supplied to the class classification unit 117.

  The processing information generated by the processing information generation unit 120 is supplied to the tap extraction units 115 and 116. The processing information supplied from the processing information generation unit 120 is supplied to the tap extraction units 115 and 116. Respectively constitute a prediction tap and a class tap having a tap structure represented by.

  The class classifying unit 117 performs the same class classification as the class classifying unit 93 in FIG. 6 based on the class tap output from the tap extracting unit 116, and adds a class code corresponding to the resulting class to the adding unit 118. Output.

  Processing information generated by the processing information generation unit 120 is supplied to the class classification unit 117. The class classification unit 117 generates a class classification represented by the processing information supplied from the processing information generation unit 120. Classify according to the method.

  The adding unit 118 reads the target teacher pixel from the teacher data storage unit 112, and targets the target teacher pixel and the student data constituting the prediction tap configured for the target teacher pixel supplied from the tap extraction unit 115. The addition is performed for each class code supplied from the class classification unit 117.

That is, the adding unit 118 uses the prediction tap (student data) x _{i, k} (x _{j, k} ) for each class corresponding to the class code supplied from the class classification unit 117, and calculates the left side of Expression (8). And multiplication (xi _{, kxj, k} ) of student data for finding a component in the matrix of and a calculation corresponding to a summation (Σ).

Further, the adding unit 118 also uses the prediction tap (student data) x _{i, k} and the teacher data y _k for each class corresponding to the class code supplied from the class classification unit 117, and calculates the equation (8). performing the student data x _i for obtaining the components in the vector of the right _{side, k} and multiplying the teacher data _{y k (x i, k y} k) and, a calculation corresponding to summation (sigma).

That is, the adding unit 118 stores the components of the matrix on the left side and the components of the vector on the right side in Equation (8), which were obtained for the teacher data set as the target teacher pixel last time, in its internal memory (not shown). And the teacher data y _k and the student data x _{i, k} (x _{j, k} ) are used for the teacher data newly set as the attention teacher pixel for the matrix component or the vector component. The corresponding components x _{i, k xj, k} or x _{i, k} y _k , respectively, which are calculated as follows, are added.

  Then, the adding unit 118 performs the above-described addition using all the teacher data stored in the teacher data storage unit 112 as the teacher pixel of interest, thereby obtaining a normal equation shown in Expression (8) for each class. Then, the normal equation is supplied to the tap coefficient calculation unit 119.

The tap coefficient calculation unit 119 obtains and outputs a tap coefficient w _n for each class by solving a normal equation for each class supplied from the adding unit 118.

Processing information generation unit 120 includes tap structure of the prediction taps and the class tap is configured in the image processing card 13 ₁ to 13 _6, respectively and, the image processing card 13 ₁ through 13 ₆ classification method of classification to perform each and The processing information is generated and supplied to the tap extraction units 115 and 116 and the class classification unit 117. Note that the tap structure, class classification method, and the like included in the processing information in the processing information generation unit 120 are stored (registered) in the processing information generation unit 120 in advance.

  Next, the processing (learning processing) of the learning device in FIG. 9 will be described with reference to the flowchart in FIG.

  First, in step S21, the teacher data generation unit 111 and the student data generation unit 113 respectively generate and output teacher data and student data from the learning image data. That is, the teacher data generation unit 111 outputs the learning image data as it is as the teacher data. In addition, the student data generation unit 111 generates and outputs student data for teacher data (learning image data) of each frame (or field) by filtering the learning image data with an LPF.

  The teacher data output by the teacher data generation unit 111 is supplied to and stored in the teacher data storage unit 112, and the student data output by the student data generation unit 113 is supplied to and stored in the student data storage unit 114.

  Thereafter, the process proceeds to step S22, in which the processing information generating unit 120 generates predetermined processing information and supplies the processing information to the tap extracting units 115 and 116 and the classifying unit 117. , The tap structure of the class tap to be configured by the tap extraction unit 116, and the class classification method in the class classification unit 177 are set.

  Then, the process proceeds to step S23, where the tap extracting unit 115 sets the teacher data stored in the teacher data storage unit 112 that has not been set as the target teacher pixel yet as the target teacher pixel. Further, in step S23, the tap extracting unit 115 configures a prediction tap from the student data stored in the student data storage unit 114 for the attention teacher pixel, supplies the predicted tap to the adding unit 118, and the tap extracting unit 116 Again, for the noted teacher pixel, a class tap is formed from the student data stored in the student data storage unit 114 and supplied to the class classification unit 117.

  Note that the tap extracting unit 115 forms a prediction tap having the tap structure set in step S22. The tap extracting unit 116 also configures the class tap having the tap structure set in step S22.

  Thereafter, the process proceeds to step S24, where the class classification unit 117 classifies the target teacher pixel based on the class tap of the target teacher pixel, and outputs a class code corresponding to the resulting class to the adding unit 118. Then, the process proceeds to step S25.

  Note that the class classification unit 117 performs the class classification according to the class classification method set in step S22.

In step S25, the adding unit 118 reads the teacher pixel of interest from the teacher data storage unit 112, and uses the teacher pixel of interest and the prediction tap supplied from the tap extraction unit 115 to calculate the matrix on the left side in Expression (8). The component x _{i, K} x _{j, K} and the component x _{i, K} y _K of the vector on the right side are calculated. Further, the adding unit 118 compares the matrix component obtained from the target pixel and the prediction tap with respect to the component corresponding to the class code from the class classification unit 117 among the components of the matrix and the components of the vector already obtained. component x _{i, K} x _j, summing component x _i of the _K-vector to _K y _K respectively, the process proceeds to step S26.

  In step S26, the tap extracting unit 115 determines whether or not the teacher data storage unit 112 has stored teacher data that is not yet set as the target teacher pixel. In step S26, when it is determined that the teacher data that is not the attention teacher pixel is still stored in the teacher data storage unit 112, the tap extraction unit 115 newly adds the teacher data that is not the attention teacher pixel yet. The process returns to step S23 as the attention teacher pixel, and the same processing is repeated thereafter.

  Also, in step S26, when it is determined that the teacher data not serving as the target teacher pixel is not stored in the teacher data storage unit 112, the adding unit 118 calculates the expression (class) for each class obtained by the above processing. The matrix on the left side and the vector on the right side in 8) are supplied to the tap coefficient calculation unit 119, and the process proceeds to step S27.

In step S27, the tap coefficient calculation unit 119 solves the normal equation for each class constituted by the matrix on the left side and the vector on the right side in the expression (8) for each class supplied from the adding unit 118, and Each time, the tap coefficient w _n is obtained and output, and the process is terminated.

Here, a class in which the number of normal equations required for obtaining the tap coefficient w _n cannot be obtained may occur due to an insufficient number of learning image data or the like. For, the tap coefficient calculation unit 119 outputs, for example, a default tap coefficient.

  In the above case, the learning image data is used as it is as the teacher data corresponding to the second image data, and the low-quality image data obtained by degrading the spatial resolution of the learning image data is used as the first image data. Since the learning of the tap coefficient is performed as the student data corresponding to the image data, the resolution improvement processing for converting the first image data into the second image data whose resolution is improved as the tap coefficient is performed. Which performs data conversion processing as described above.

  Therefore, by storing the tap coefficients in the coefficient memory 94 (FIG. 6) of the image processing card 13, the image processing card 13 can improve the spatial resolution of the image data.

  Here, depending on the method of selecting the student data corresponding to the first image data and the image data serving as the teacher data corresponding to the second image data, tap coefficients for performing various data conversion processes are obtained. be able to.

  That is, for example, high-quality image data is used as teacher data, and learning processing is performed on the high-quality image data as the teacher data using image data on which noise is superimposed as student data. In addition, it is possible to obtain an image obtained by performing a data conversion process as a noise removal process for converting the first image data into the second image data in which noise included therein has been removed (reduced).

  In addition, for example, while certain image data is used as teacher data, image data obtained by thinning out the number of pixels of the image data as teacher data is used as student data, or image data of a predetermined size is used as student data, and A learning process is performed by using image data obtained by thinning pixels of image data as data at a predetermined thinning rate as teacher data, and as a tap coefficient, second image data obtained by enlarging or reducing the first image data It is possible to obtain a device that performs a data conversion process as a resize process for converting the data into a data size.

  Therefore, by storing a tap coefficient for noise removal processing and a tap coefficient for resizing processing in the coefficient memory 94 (FIG. 6) of the image processing card 13, the image processing card 13 allows the image processing card 13 to remove noise and resize image data. (Enlarge or reduce).

As described above, depending on the set of teacher data and student data used for learning tap coefficients (hereinafter also referred to as a learning pair as appropriate), various image qualities such as improvement in resolution, noise removal, and resizing can be used as tap coefficients. improvements can be obtained to perform, therefore, a tap coefficient for performing such various improvements in image quality, by storing the image processing card 13 ₁ to 13 _6, in each of the image processing cards 13 _i, various Data conversion processing for improving image quality can be performed.

  In addition, for example, the learning process is performed by using the high-quality image data as teacher data, reducing the spatial resolution of the high-quality image data as the teacher data, and further using the image data on which noise is superimposed as student data. Accordingly, as a tap coefficient, noise removal processing and resolution improvement for removing (reducing) noise included in the first image data and further converting the first image data into second image data having an improved spatial resolution are used. It is possible to obtain one that performs both processes as a data conversion process.

  However, image data that has been subjected to data conversion processing using tap coefficients that perform both noise removal processing and resolution improvement processing, and data conversion processing that uses tap coefficients that perform noise removal processing, and further, resolution improvement processing If the conditions of each data conversion process are the same, there is a difference in image quality between the image data subjected to the data conversion process using the tap coefficient to be performed.

  That is, data conversion processing using tap coefficients for performing both noise removal processing and resolution improvement processing, data conversion processing using tap coefficients for noise removal processing, and data conversion processing using tap coefficients for performing resolution improvement processing In both cases, when the prediction tap and the class tap having the same tap structure are used and the same class classification method (the same number of classes) is employed, data conversion processing is performed using tap coefficients for performing noise removal processing. The image data that has been subjected to the data conversion processing using the tap coefficient for performing the resolution improvement processing is further subjected to the data conversion processing using the tap coefficient for performing both the noise removal processing and the resolution improvement processing. Higher image quality than data.

  By the way, in the generation information storage unit 97 of the image processing card 13, it is possible to store the tap coefficient for each class obtained by the learning device of FIG. 9 as the generation information.

However, in this case, the number of classes is Z, and the capacity of a set of tap coefficients per class (a set of tap coefficients w ₁ , w ₂ ,..., W _N that defines equation (1)) is B. In this case, the generation information storage unit 97 constituting the image processing card 13 needs a memory having at least a capacity of Z × B.

Therefore, here, the tap coefficients w _1, w _2, · · ·, as the tap number suffix n in the n-th tap coefficient w _n of the set of w _N, a tap coefficient for each class w ₁ to w _N For example, compression can be performed for each tap number, and the compression result can be stored in the generation information storage unit 97 (FIG. 6) as generation information. In this case, the storage capacity required for the generation information storage unit 97 can be reduced.

The compression of the tap coefficients w _{1 to} w _N for each class for each tap number can be performed, for example, as follows.

That is, it is considered that the tap coefficient w _n is generated from the coefficient seed data serving as the seed and a predetermined parameter according to, for example, the following equation.

・・・（９）

... (9)

In Expression (9), β _{m, n} represents the m-th coefficient seed data used for obtaining the n-th tap coefficient w _n , and z represents a parameter. In the equation (9), the tap coefficient w _n is obtained using M pieces of coefficient seed data β _{n, 1} , β _{n, 2} ,..., Β _{n, M.}

Here, the equation for calculating the tap coefficient w _n from the coefficient seed data β _{m, n} and the parameter z is not limited to equation (9).

Now, a value z ^m-1 determined by the parameter z in the equation (9) is defined by the following equation by introducing a new variable t _m .

・・・（１０）

... (10)

  By substituting equation (10) into equation (9), the following equation is obtained.

・・・（１１）

... (11)

According to equation (11), the tap coefficient w _n is obtained by a linear first-order equation of the coefficient seed data beta _{n, m} and the variable t _m becomes the (predicted) that, now, the formula (11 the tap coefficient obtained by), 'When is represented as is expressed by the following equation (12), the tap coefficient w _n which is determined by the optimum tap coefficients w _n and equation (11)' w _n error between the e _n is the coefficient seed data beta _{n, m} to 0 is the optimal for determining the optimum tap coefficients w _n, for all of the tap coefficients w _n, such coefficient seed data beta _{n, m} Is generally difficult to find.

・・・（１２）

... (12)

Expression (12) can be modified as follows by substituting expression (11) into w _n ′ on the right side.

・・・（１３）

... (13)

Therefore, assuming that the least squares method is adopted as a criterion indicating that the coefficient seed data β _{n, m} is optimal, the optimal coefficient seed data β _{n, m} is expressed by the following equation. It can be obtained by minimizing the sum E of the squared errors.

・・・（１４）

... (14)

The minimum value (minimum value) of the sum E of the squared errors in the equation (14) is, as shown in the equation (15), βn _, which is obtained by partially differentiating the sum E with the coefficient seed data βn _{, m .} given by _m .

・・・（１５）

... (15)

  By substituting equation (13) into equation (15), the following equation is obtained.

・・・（１６）

... (16)

Now, X _{i, j,} and Y _i are defined as shown in Expressions (17) and (18).

・・・（１７）

... (17)

・・・（１８）

... (18)

In this case, equation (16) can be represented by a normal equation shown in equation (19) using X _{i, j} and Y _i .

・・・（１９）

... (19)

The normal equation of Expression (19) can also be solved for the coefficient seed data β _{n, m} by using, for example, a sweeping-out method (Gauss-Jordan elimination method).

According to the coefficient seed data β _{n, m} obtained by solving the normal equation of Expression (19), a tap coefficient w _n can be obtained for each parameter z according to Expression (9).

By the way, when the class of the tap coefficient is adopted as the parameter z, for example, the set of the tap coefficient of the class #z is obtained from the equation (9) by the parameter (class) z and the coefficient seed data β _{n, m.} Can be requested.

Therefore, assuming that the n-th tap coefficient w _n of the class #z is represented by w ^(z) _n , the n-th tap coefficient w ^(z) _n of the class #z is expressed by Expression (9). Therefore, the coefficient seed data β _{n, m to} be obtained is the n-th tap coefficient w ⁽¹⁾ _n , w ^{(2 ) of} each of the classes # 1, # 2,..., #Z obtained by the learning device of FIG. ⁾ _n ,..., w ^(Z) _n are used as teacher data, and parameters # 1, # 2,. ) Can be obtained by setting up and solving the normal equation.

FIG. 11 shows an example of the configuration of a learning device that performs learning for obtaining coefficient seed data β _{n, m} for obtaining the n-th tap coefficient w ^(z) _n of class #z as described above. In the figure, portions corresponding to the case in FIG. 9 are denoted by the same reference numerals, and the description thereof will be appropriately omitted below.

  The tap coefficient memory 121 stores a set of tap coefficients for each class output by the tap coefficient calculation unit 119.

The adding unit 122 targets, for each tap number n, a parameter #z (a variable t _m corresponding to the class) representing a class and a tap coefficient w ^(z) _n of each class #z stored in the tap coefficient memory 121. And add.

That is, the adding unit 122 uses the variable t _i (t _j ) obtained by the equation (10) from the parameter z and uses the component X _i, defined by the equation (17) in the matrix on the left side of the equation (19) _{. For} each tap number n, a multiplication (t _i t _j ) of variables t _i (t _j ) corresponding to a parameter z for obtaining _j and an operation corresponding to a summation (Σ) are performed.

Further, the adding unit 122 uses the variable t _i obtained by the equation (10) from the parameter z and the tap coefficient w ^(z) _n and defines the equation (18) in the vector on the right side of the equation (19). The multiplication (t _i w _n ) of the variable t _i and the tap coefficient w _n corresponding to the parameter z for _{obtaining the} component Y _i to be performed, and the operation corresponding to the summation (Σ) are performed for each tap number n.

The adding unit 122 obtains, for each tap number n, a component X _{i, j} expressed by Expression (17) and a component Y _i expressed by Expression (18). When the normal equation of (19) is established, the normal equation is supplied to the coefficient type calculating unit 123.

In the embodiment of FIG. 11, the processing information output by the processing information generating unit 120 is supplied to the adding unit 122. In this case, the processing information includes M representing the number of terms of equation (9) from the coefficient seed data beta _{m, n} as a tap calculation equation for determining the tap coefficient w ^(z) _n, the adder 122 Refers to the processing information supplied from the information processing generation unit 120, thereby recognizing the number M of terms in the tap operation expression, and forming the normal equation of Expression (19).

The coefficient seed calculating unit 123 obtains and outputs coefficient seed data β _{m, n} for each tap number n by solving the normal equation of Expression (19) for each tap number n supplied from the adding unit 122. .

  Next, the processing (learning processing) of the learning device in FIG. 11 will be described with reference to the flowchart in FIG.

  In steps S31 to S37, the same processes as those in steps S21 to S27 in FIG. 10 are performed, whereby the tap coefficients for each class obtained by the tap coefficient calculation unit 119 are supplied to the tap coefficient memory 121. It is memorized.

  In step S32, in addition to the tap structure of the prediction tap to be configured by the tap extraction unit 115, the tap structure of the class tap to be configured by the tap extraction unit 116, the class classification method in the class classification unit 117, the addition unit 122 The number of terms M of the tap operation expression (9) required to form the normal equation of (19) is also set.

That is, the processing information generation unit 120 in the learning device of FIG. 11 includes a tap structure of a prediction tap and a class tap configured on each of the image processing cards 13 _{1 to} 13 ₆ , and a class configured on the image processing cards 13 _{1 to} 13 _6. other classification methods classification, produce a number of terms processing information M including the tap calculation equation (9) for generating the tap coefficient w _n in the image processing card 13 ₁ to 13 ₆ from the coefficient seed data beta _{m, n} Then, the data is supplied to the tap extracting units 115 and 116, the class classifying unit 117, and the adding unit 122. The adding unit 122 calculates the equation (19) according to the number M of terms included in the processing information. The operation mode is set so that the normal equation of

Thereafter, the process proceeds to step S38, where the adding unit 122 determines, for each tap number n, a parameter #z (corresponding to a variable t _m ) representing a class and a tap coefficient of each class #z stored in the tap coefficient memory 121. The above-described addition is performed on w ^(z) _n , thereby forming a normal equation of Expression (19) for each tap number n and supplying the normal equation to the coefficient type calculation unit 123, and then proceeds to step S 39. Proceed to.

  In step S38, the adding unit 122 forms a normal equation of equation (19) according to the number M of terms of the tap operation equation (9) set in step S32.

In step S39, the coefficient seed calculation unit 123 solves the normal equation of the equation (19) for each tap number n supplied from the adding unit 122, thereby obtaining the coefficient seed data β _{m, n} for each tap number n. The output is obtained and the processing is terminated.

In the learning device in FIG. 11, the learning pair and the processing information are appropriately changed to perform the learning process in FIG. 12, whereby the coefficient seed data β _{m, n} that can perform various image quality improvements is obtained. Then, in this way ones of the coefficient seed data beta _{m, n} which can perform various improvements of the image quality obtained by different, it is stored in the image processing card 13 ₁ to 13 _6, respectively.

Assuming that the number of tap coefficients w _n in one class is N and the number of classes is Z class, the total number of tap coefficients w _n is N × Z.

On the other hand, _{assuming that} the number of tap coefficients w _n in one class is N and the number of terms in the tap operation expression (9) is M, the total number of coefficient seed data β _{m, n} is N × M. .

Therefore, _assuming that one tap coefficient w _n and coefficient seed data β _{m, n} have the same data amount, the data amount of all coefficient seed data β _{m, n} is _{equal to} the data amount of all tap coefficients w _n . M / Z.

Therefore, when M is compared with Z, M is the number of terms in the tap operation expression (9), and a value of one digit or at most about two digits such as, for example, ten is adopted. On the other hand, Z represents the number of classes, and the class classification for obtaining the class is obtained, for example, by performing ADRC processing on the class tap as described above. Therefore, for example, if class classification is performed by performing 1-bit ADRC processing on a class tap composed of 25 pixels of 5 × 5 pixels, the number of classes becomes ²²⁵ classes.

From the above, it can be said that M / Z is a sufficiently small value, and therefore, the tap coefficient w _n is compressed into the coefficient seed data β _{m, n} having a sufficiently small data amount.

Therefore, by storing the coefficient seed data β _{m, n} as generation information in the generation information storage unit 97 (FIG. 6), it is necessary for the generation information storage unit 97 to store the tap coefficients w _n compared to the case where the tap coefficients w _n are stored. Storage capacity can be greatly reduced.

Here, the processing information stored in the image processing card 13 _i shown in FIG. 6 includes, in addition to the tap structure and the class classification method, the number M of terms in the tap operation expression (9), the class The number of classes Z by classification is also included, and the coefficient generation unit 96 recognizes the number M of terms in the tap operation expression (9) by referring to the processing information. Further, the card controller 98 recognizes the number of classes Z by referring to the processing information, and gives an integer value of 1 to Z to the coefficient generation unit 96 as a parameter z. Then, the coefficient generation unit 96 uses the coefficient seed data β _{m, n} as generation information and the parameter z supplied from the card controller 98 to calculate the summation of the M term on the right side of the tap operation expression (9). calculated, thereby determining the tap coefficient w _n for each class.

  Next, as described above, according to the learning apparatus of FIG. 11, as shown in FIG. 13, high-quality image data is used as teacher data, and low-quality image data whose image quality is reduced is used as student data. Is performed to generate a set of tap coefficients for the Z class. Further, according to the learning apparatus of FIG. 11, the set of tap coefficients for the Z class is compressed, and a set of coefficient seed data for N taps, which is the number of taps of the prediction tap, is generated.

  Then, depending on what learning pair (teacher data and student data) is used in the learning, it is possible to generate tap coefficients for performing different types of image quality improvement (for example, noise reduction or resolution improvement). Coefficient seed data can be obtained. Further, the tap structure of the prediction tap employed in the learning (including the number of taps in addition to the positional relationship of the pixels to be the prediction tap), the tap structure of the class tap, the class classification method, and the number of terms M in the tap operation formula (9) For example, coefficient seed data that can generate tap coefficients with different degrees of image quality improvement can be obtained.

In each of the image processing cards 13 _{1 to} 13 ₆ , coefficient type data for generating tap coefficients w _n for improving the image quality of different types and degrees can be stored as generation information. Referring to Figure 19, in the image processing card 13 ₁ through 13 _6, for example, the image processing card 13 ₁ and 13 ₂ example of coefficient seed data stored in the will be described.

First, the image processing card 13 _1, for example, as shown in FIG. 14, the learning quality image data as well as the teaching data, to reduce its resolution, and the low-quality image data obtained by adding noise as student data Is performed, a set of tap coefficients for 64 classes for resolution improvement / noise removal for improving resolution and removing noise is generated, and the set of tap coefficients for 64 classes is compressed to improve resolution / A set of coefficient seed data for nine taps for noise removal can be generated, and the set of coefficient seed data for nine taps can be stored as generation information.

The image processing card 13 _1, in addition to the set of 9 taps of the coefficient seed data for resolution enhancement / noise removal in FIG. 14, for example, as shown in FIG. 15, a high-quality image data training data At the same time, learning is performed using the low-quality image data whose resolution has been reduced as student data, a set of tap coefficients for 64 classes for resolution improvement for improving resolution is generated, and further tap coefficients for the 64 classes are generated. Is compressed to generate a set of coefficient seed data for 25 taps for resolution improvement, and the set of coefficient seed data for 25 taps can be stored as generation information.

On the other hand, the image processing card 13 _2, for example, as shown in FIG. 16 performs learning as well as a high-quality image data and the teacher data, it quality image data obtained by adding noise as student data, removes noise Generating a set of tap coefficients for 64 classes for noise removal, further compressing the set of tap coefficients for 64 classes, and generating a set of coefficient seed data for 25 taps for noise removal. A set of coefficient seed data for 25 taps can be stored as generation information.

In this case, the image processing interface 40, when only the image processing card 13 ₁ is mounted, the image processing card 13 _1, for example, as shown in FIG. 17, 9 coefficients of taps species for resolution enhancement / noise removal For example, from a set of data and a set of coefficient seed data for 25 taps for resolution improvement, a set of coefficient seed data for 9 taps for resolution improvement / noise removal is replaced with 64 sets for resolution improvement / noise removal. A set of tap coefficients for the class is generated. In the image processing card 13 _1, using the set of tap coefficients of the 64 classes minute for resolution enhancement / noise removal, for the first image data supplied from the frame memory 35 (FIG. 3), the data conversion process Then, the second image data with improved resolution and noise removed is output.

Note that the image processing card 13 _first generation information storage unit 97 stored processing information (FIG. 6), from a set of 9 taps of the coefficient seed data for resolution enhancement / noise removal to the effect of generating a tap coefficient information are included, the image processing card 13 ₁ of the card controller 98 (FIG. 6), by the coefficient generating unit 96 is controlled in accordance with the processing information, 9 coefficients of taps for resolution enhancement / noise removal From the set of seed data, a set of tap coefficients for 64 classes for resolution improvement / noise removal is generated.

On the other hand, the image processing interface 40, when the image processing card 13 ₁ and 13 ₂ is attached, in the image processing card 13 _1, for example, as shown in FIG. 18, 9 coefficients of taps for resolution enhancement / noise removal From a set of seed data and a set of coefficient seed data for 25 taps for resolution improvement, for example, a set of coefficient seed data for 25 taps for resolution improvement is changed to a tap coefficient for 64 classes for resolution improvement. Is generated.

In the image processing card 13 _2, as shown in FIG. 18, from 25 sets of coefficient seed data of taps for removing noise, a set of tap coefficients for 64 classes minute for removing noise is generated.

In the image processing card 13 _1, using the set of tap coefficients of the 64 classes minute for resolution enhancement, for the first image data supplied from the frame memory 35 (FIG. 3), the data conversion processing is performed, Thereby, the second image data with improved resolution is output.

The second image data, via the card interface 83 of the image processing interface 40, the image processing card 13 ₂ is supplied as the first image data.

In the image processing card 13 _2, using a set of 64 classes amount of tap coefficients for noise removal, for the first image data supplied thereto, the data conversion processing is performed, thereby, noise is removed The second image data is output.

That is, the image data stored in the frame memory 35 is subjected to data conversion processing in each of the image processing card 13 ₁ and the image processing card 13 ₂ , so that the image data is improved in resolution and noise is removed. .

Note that the image processing card 13 _second generation information storage unit 97 stored processing information (FIG. 6), the image processing card 13 _1, the tap coefficients from the 25 sets of coefficient seed data of taps for resolution enhancement information indicating that generated are included, in the image processing card 13 ₁ of the card controller 98 (FIG. 6), when the image processing card 13 _second processing information (now, processing information of the image processing cards 13 ₂ up the ID process information, as described with reference to FIG. 8, the image processing card 13 _1, according to the maximum ID process information, by the coefficient generating unit 96 is controlled in accordance with the tap coefficients are generated), resolution enhancement A set of tap coefficients for 64 classes for resolution improvement is generated from the set of coefficient seed data for 25 taps for the resolution.

Here, even if only the image processing card 13 ₁ is mounted, even when the image processing card 13 ₁ and 13 ₂ is mounted, image data finally obtained, improved resolution, and the noise is removed Image data.

However, when only the image processing card 13 ₁ is mounted, the image processing card 13 ₁ uses a set of tap coefficients for 64 classes for resolution improvement / noise removal, which is also used for improving resolution and removing noise. The data conversion process is performed using the data. On the other hand, when the image processing cards 13 ₁ and 13 ₂ are mounted, the image processing card 13 ₁ performs data conversion using a set of tap coefficients for 64 classes for resolution improvement, so to speak, in order to improve the resolution. process is performed, further, in the image processing card 13 _2, the data conversion processing using the set of tap coefficients of the 64 classes minute for dedicated noise removal noise removal is performed.

Therefore, even when only the image processing card 13 ₁ is mounted, image data with improved resolution and noise removed can be obtained, but the image processing cards 13 ₁ and 13 ₂ are mounted. In this case, it is possible to obtain image data in which the resolution is further improved and the noise is further removed.

That is, after the image processing card 13 ₁ is mounted, the image processing card 13 ₂ is additionally mounted, so that the main body 1 (FIG. 1) of the television receiver additionally has a higher function.

As a result, the user has an incentive to purchase the image processing card 13 ₁ and further purchase the image processing card 13 ₂ .

Here, in the case described above, in the image processing card 13 _1, when only the image processing card 13 ₁ is mounted, from a set of 9 taps of the coefficient seed data for resolution enhancement / noise removal, resolution enhancement / noise removal A set of tap coefficients for 64 classes for resolution is generated, and when the image processing cards 13 ₁ and 13 ₂ are attached, a set of coefficient seed data for 25 taps for resolution improvement is obtained from a set of coefficient seed data for 25 taps for resolution improvement. Are generated. Accordingly, the number of taps of the prediction tap when only the image processing card 13 ₁ is mounted is nine, whereas the number of taps of the prediction tap when the image processing cards 13 ₁ and 13 ₂ are mounted is 25. a tap, thus, from the often taps of the prediction taps, the degree of resolution enhancement when the image processing card 13 ₁ and 13 ₂ is mounted, typically, only the image processing card 13 ₁ is mounted This is larger than the degree of improvement in resolution.

In the case described above, if focusing on the main body 1 of the television receiver, from a state in which only the image processing card 13 ₁ is attached, by a state in which the image processing card 13 ₁ and 13 ₂ is attached, The function of improving the resolution and the function of removing noise are further improved, and the type of the function itself does not change. On the other hand, if focusing on only the image processing card 13 _1, the image processing card 13 ₁ functions, noise improves resolution, and the function of removing the noise, will be changed by the function of improving the resolution, The type of the function itself will change.

Further, for example, the image processing cards 13 _3, the coefficient seed data for generating tap coefficients for enlargement for performing image enlargement, can be memorized as product information, in this case, the image processing card 13 ₁ Tap coefficient for enlargement / noise removal / resolution improvement for enlarging / removing noise and improving resolution, and tap coefficient for enlargement / resolution improvement for enlarging / improving resolution Can be stored.

In this case, when only the image processing card 13 ₁ is attached, in the image processing card 13 _1, it is possible to perform the data conversion process by using the tap coefficients for enlargement / noise removal / resolution enhancement. Further, when the image processing cards 13 ₁ and 13 ₂ are attached, the image processing card 13 ₁ performs data conversion processing using tap coefficients for enlargement / resolution, and the image processing card 13 ₂ performs noise conversion. Data conversion processing can be performed using the tap coefficients for removal. Further, when the image processing cards 13 _{1 to} 13 ₃ are attached, the image processing cards 13 _{1 to} 13 ₃ use a tap coefficient for improving resolution, a tap coefficient for removing noise, and a tap coefficient for enlarging. , Respectively, can perform data conversion processing.

Next, in the above case, the image processing card 13 _2, but so as to store the set itself coefficient seed data of 25 taps for noise removal, image processing card 13 _2, 19 as such, 25 and taps of the coefficient seed data for the noise removal, the set of difference data between the 25 taps of the coefficient seed data for resolution enhancement, which is stored in the image processing card 13 ₁ as allowed to store It is possible to do.

In this case, the image processing card 13 ₁ and 13 ₂ is mounted to the image processing interface 40, the image processing card 13 _2, the card controller 98 (FIG. 6), the card interface 83 of the image processing interface 40 (FIG. 5) The coefficient seed data for 25 taps for improving the resolution is read out from the image processing card 13 ₁ via the CPU and added to the difference data stored in the image processing card 13 ₂ , whereby the 25 taps for noise removal are read out. Generate coefficient seed data. Then, the card controller 98 supplies the coefficient seed data for 25 taps for noise removal to the coefficient generator 96 (FIG. 6) to generate tap coefficients for 64 classes for noise removal.

Here, generation information stored in the image processing card 13 _2, and it is difference data as described above, restoration process of the difference data, for example, describes the image processing card 13 ₂ processes information can be placed, therefore, the card controller 98 of the image processing cards 13 ₂ refers to the processing information thereof, the difference data as the generation information, to restore the coefficient seed data.

In this case, the set of tap coefficients for 64 classes for noise removal used by the image processing card 13 ₂ in the data conversion processing does not have coefficient seed data for 25 taps for resolution improvement stored in the image processing card 13 _1. Cannot be generated, a user who does not have the image processing card 13 ₁ illegally acquires a set of tap coefficients for 64 classes for noise removal used in the data conversion processing in the image processing card 13 ₂ . Trying to do so can be prevented.

Note that the other image processing card 13 _i also includes coefficient seed data for generating tap coefficients used by the image processing card 13 _i for data conversion processing, and an image processing card 13 with a card ID one rank higher than that. It is possible to store difference data with coefficient seed data for generating tap coefficients used by _i-1 for data conversion processing.

Next, FIG. 20 shows another configuration example of the image processing card _13i . In the figure, portions corresponding to those in FIG. 6 are denoted by the same reference numerals, and description thereof will be omitted below as appropriate. That is, the image processing card 13 _i of FIG. 20 is provided with a memory space sharing control unit 100 instead of the coefficient memory 94, and the coefficient memory 94 is connected to the memory space sharing control unit 100. Otherwise, the configuration is basically the same as in FIG.

Memory space shared control unit 100 controls the memory space of the coefficient memory 94 of the image processing cards 13 _i have, sharing with the memory space of the coefficient memory 94 with the other image processing card 13 i _'.

That is, when the capacity of the coefficient memory 94 of the image processing card 13 _i alone is not sufficient, the memory space sharing control unit 100 sends another image processing card via the card interface 83 of the image processing interface 40 (FIG. 5). 13 _{i ′} communicates with the memory space sharing control unit 100, and when the capacity of the coefficient memory 94 in the other image processing card 13 _{i ′} is surplus, the remaining memory space and the image processing card 13 _i The real memory space of the coefficient memory 94 is a virtual memory space (virtual memory space) of the image processing card _13i .

Thereby, the memory space sharing control unit 100 stores the set of tap coefficients output by the coefficient normalizing unit 99 in the virtual memory space (the real memory space of the coefficient memory 94 in the image processing card 13 _i , or the other image processing card 13 _{i '} ) (in the actual memory space of the coefficient memory 94). Further, upon receiving the class code from the class classification unit 93, the memory space sharing control unit 100 reads a set of tap coefficients of the class corresponding to the class code from the virtual memory space and supplies the set of tap coefficients to the prediction unit 95.

  Therefore, from the coefficient normalizing unit 99 and the classifying unit 93, the memory space sharing control unit 100 looks equivalent to the coefficient memory 94 in the embodiment of FIG.

  Note that what capacity of the memory space is required as the virtual memory space depends on the capacity of the tap coefficients stored in the virtual memory space. Then, assuming that the size of one tap coefficient is known, the capacity of the tap coefficient to be stored in the virtual memory space can be calculated from the number of taps N and the number of classes Z of the prediction tap. Therefore, the card controller 98 calculates the total capacity of the tap coefficients generated by the coefficient generation unit 96 by referring to the number of taps N and the number of classes Z of the prediction taps described in the processing information of the generation information storage unit 97. Then, this is supplied to the memory space sharing control unit 100 as a required memory space size.

  As described above, the memory space sharing control unit 100 secures a virtual memory space having a size of a necessary memory space supplied from the card controller 98.

Also, the memory space shared control unit 100, in the case where the capacity of the coefficient memory 94 of the image processing cards 13 _i has left over in response to a request from the memory space shared control unit 100 of the other image processing card 13 i _', The use of the surplus memory space is permitted, and the rest is reserved and used as the virtual memory space of the image processing card _13i .

  The reason why the coefficient memory 94 is shared among the plurality of image processing cards 13 as described above is as follows.

  That is, the embodiment of FIG. 6 does not particularly refer to the capacity of the coefficient memory 94, and therefore has a sufficient storage capacity to store the tap coefficients output from the coefficient normalization unit 99.

Thus, for example, in the case described in the embodiment of FIG. 17, the image processing card 13 _1, from a set of coefficient seed data of 9 taps for resolution enhancement / noise removal, 64 classes for resolution enhancement / noise removal Since a set of tap coefficients for each class is generated, a set of tap coefficients of 9 taps per class for the 64 classes is stored. If the size of one tap coefficient is S, 64 × 9 × A capacity of size S is required.

On the other hand, for example, in the case described in the embodiment of FIG. 18, the image processing card 13 _1, from a set of coefficient seed data of 25 taps for resolution enhancement, the set of tap coefficients for 64 classes amount for resolution enhancement Is generated, a capacity of 64 × 25 × S is required to store a set of tap coefficients of 25 taps per class for 64 classes.

Thus, in the embodiment of FIG. 6, the coefficient memory 94 of the image processing cards 13 ₁ requires a capacity of the size of 64 × 25 × S.

However, in the coefficient memory 94, what the overall size of the 64 × 25 × S is used, a case where the image processing card 13 ₁ and 13 ₂ has been mounted, only the image processing card 13 ₁ is attached In this case, only the size of 64 × 9 × S is used, and in this case, a capacity close to 2/3 (≒ (25−9) / 25) of the coefficient memory 94 is wasted.

  Therefore, in the embodiment of FIG. 20, the coefficient memory 94 has a predetermined number of taps N and a set of tap coefficients of a predetermined number of classes Z that does not generate such useless capacity. Storage capacity.

That is, again, will be described an image processing card 13 ₁ and 13 ₂ as an example, the image processing card 13 _1, as a generation information, for example, as shown in FIG. 21, the coefficient seed data for resolution enhancement / noise removal and set, and a set of coefficient seed data for resolution enhancement is stored, if only image processing card 13 ₁ has been attached to the image processing interface 40, as shown in FIG. 21, resolution enhancement / from a set of coefficient seed data for noise removal, a set of 64 classes amount of tap coefficients for resolution enhancement / noise removal is generated, also, the image processing card 13 ₁ and 13 ₂ has been attached to the image processing interface 40 In this case, as shown in FIG. 22, a set of tap coefficients for 48 classes for resolution improvement is generated.

On the other hand, the image processing card 13 _2, as a generation information, for example, as shown in FIG. 22, a set of coefficient seed data for noise removal is stored, the image processing card 13 ₁ and 13 _2, the image processing When it is attached to the interface 40, a set of tap coefficients for 80 classes for noise removal is generated from a set of coefficient seed data for noise removal.

  In this case, for simplicity of explanation, the number of taps of the prediction taps, that is, the number of taps of the tap coefficient per class is ignored assuming that N is constant.

Each of the coefficient memories 94 of the image processing cards 13 ₁ and 13 ₂ has a capacity corresponding to a set of tap coefficients for 64 classes.

In this case, only the image processing card 13 _1, when attached to the image processing interface 40, in the image processing card 13 _1, as shown in FIG. 23, generated from a set of coefficient seed data for resolution enhancement / noise removal is the set of tap coefficients of the 64 classes minute for resolution enhancement / noise removal, it can be stored in the image processing card 13 ₁ of the coefficient memory 94. In this case, the capacity of the image processing cards 13 ₁ of the coefficient memory 94, are identical to the size of the set of generated 64 class content of the tap coefficients, thus, the capacity of the coefficient memory 94 are not leftover.

Also, when the image processing cards 13 ₁ and 13 ₂ are attached to the image processing interface 40, the image processing card 13 ₁ generates a set of coefficient seed data for improving resolution as shown in FIG. is the set of tap coefficients of the 40 classes minute for resolution enhancement, it can be stored in the image processing card 13 ₁ of the coefficient memory 94. In this case, set the size of the tap coefficients of the 48 class fraction that is generated, than the capacity of the image processing cards 13 ₁ of the coefficient memory 94, reduced by a set amount of the tap coefficients of the 16 class component, therefore, the coefficient memory 94 The extra capacity is surplus.

However, when the image processing cards 13 ₁ and 13 ₂ are attached to the image processing interface 40, the image processing card 13 ₂ , as shown in FIG. since the 80 sets of the class content of the tap coefficients of the use is generated, the all, the image processing card 13 _2, is be stored only set amount in the capacitance of the tap coefficients of the 64 classes minute to no coefficient memory 94 Can not. That is, in this case, the image in the processing card 13 _2, sets the size of the 80 class content of the tap coefficients being generated, than the capacity of the coefficient memory 94 of the image processing cards 13 _2, 16 sets of tap coefficients for classes min min only large, therefore, its stores a set of all of the tap coefficients, only the coefficient memory 94 of the image processing cards 13 _2, lacking only the capacity of a set amount of the tap coefficients of the 16 classes minute.

Therefore, in the embodiment of FIG. 20, the image processing card 13 ₂ of the memory space shared control unit 100, as shown in FIG. 26, the image processing card 13 ₁ of the memory space shared control unit 100, the coefficient memory A request is made for a memory space R ₁ ′ having a capacity corresponding to a set of tap coefficients for 16 classes in the 94 real memory spaces R ₁ .

In this case, the image processing card 13 ₁ of the memory space shared control unit 100, the memory space required to himself, as described above, recognizes that a size capable of storing the tap coefficients of the 48 class component, as a result, since the 16 volume set amount of the tap coefficients of the class of the real memory space R ₁ of the coefficient memory 94 is left over, as shown in FIG. 26, as a virtual memory space r ₁ of its own, the 48 class min The capacity for storing the tap coefficient is secured. Further, the image processing card 13 ₁ of the memory space shared control unit 100, the image processing card 13 ₂ to the memory space shared control unit 100, 16 sets amount in the capacitance of the tap coefficients of the class partial memory space R ₁ 'of the Allow use.

Thus, the image processing card 13 ₂ of the memory space shared control unit 100, as shown in FIG. 26, a real memory space R ₂ of the image processing cards 13 ₂ of the coefficient memory 94, is allowed to use the image processing securing virtual memory space r ₂ set amount in the capacitance of the real memory space R ₁ 'and 80 tap coefficients for classes worth of combined set amount in the capacitance of the tap coefficients of the 16 classes worth of card 13 ₁ of the coefficient memory 94 Then, a set of tap coefficients for 80 classes is stored therein.

  Next, the processing of the image processing card 13 in FIG. 20 will be described with reference to the flowchart in FIG.

  In the image processing card 13 in FIG. 20, in step S51, the card controller 98 receives the processing information (maximum ID processing information) transmitted by the interface controller 81 (FIG. 5) in step S5 in FIG. Determine whether

  If it is determined in step S51 that the maximum ID processing information has not been received, steps S52 to S55 are skipped, and the process proceeds to step S56.

  If it is determined in step S51 that the maximum ID processing information has been received, the process proceeds to step S52, and the card controller 98 refers to the maximum ID processing information, and performs the same processing as in step S12 in FIG. Recognizes the contents of the data conversion process to be performed by itself, and controls the tap extracting units 91 and 92 and the classifying unit 93 according to the recognition result.

  Then, proceeding to step S53, the card controller 98 refers to the processing information (maximum ID processing information) determined to have been received from the image processing interface 40 in step S51, and thereby the tap coefficient generated by the coefficient generation unit 96 is generated. The size of the set is recognized and supplied to the memory space sharing control unit 100. That is, the maximum ID processing information also describes information on the number of classes Z and the number of taps N of the tap coefficients generated from the coefficient seed data as the generation information, and the card controller 98 determines the coefficient based on the information. The size of the tap coefficient generated by the generation unit 96 is recognized and supplied to the memory space sharing control unit 100.

  Further, in step S53, the memory space sharing control unit 100 secures a virtual memory space of the size received from the card controller 98 as described above, and proceeds to step S54.

  In step S54, the card controller 98 controls the coefficient generation unit 96 based on the maximum ID processing information to generate tap coefficients for each class from the generation information in the same manner as in step S13 in FIG. . The tap coefficient is supplied to a coefficient normalizing unit 99.

  In step S55, the coefficient normalization unit 99 normalizes the tap coefficients for each class from the coefficient generation unit 96, and supplies the normalized tap coefficients for each class to the memory space sharing control unit 100. In step S55, the memory space sharing unit 131 further stores the tap coefficients for each class from the coefficient normalization unit 99 in the virtual memory space secured in step S53, and the process proceeds to step S56.

  In step S56, the tap extracting units 91 and 92 respectively configure a prediction tap and a class tap for the pixel of interest, as in step S15 in FIG. 8, and the prediction tap is transmitted from the tap extracting unit 91 to the prediction unit 95. The supplied class taps are supplied from the tap extracting unit 92 to the classifying unit 93.

  Then, the process proceeds to step S57, where the class classification unit 93 classifies the pixel of interest based on the class tap for the pixel of interest supplied from the tap extraction unit 92, and outputs a class code representing the class to the memory space sharing control unit. 100, and the process proceeds to step S58.

  In step S58, the memory space sharing control unit 133 acquires the tap coefficient stored at the address corresponding to the class code supplied from the class classification unit 93 by reading the tap coefficient from the virtual memory space. This tap coefficient is supplied from the memory space sharing control unit 100 to the prediction unit 95, and the process proceeds to step S59.

  In step S59, the prediction unit 95 performs the prediction calculation of Expression (1) using the prediction tap output from the tap extraction unit 91 and the tap coefficient supplied from the memory space sharing control unit 100. Thereby, the prediction unit 95 obtains the pixel value of the target pixel and supplies the pixel value to the card interface 83 (FIG. 5). Then, the process returns to step S51, and the same processing is repeated thereafter.

  As described above, in the plurality of image processing cards 13, the real memory space of the coefficient memory 94 is shared, and the minimum virtual memory space necessary for storing the tap coefficients is secured. It is possible to prevent the capacity from being wasted.

Further, in the example described in FIGS. 21 to 26, the image processing card 13 _2, together with the image processing card 13 _1, unless attached to the image processing interface 40, 80 class fraction of taps generated by the coefficient generator 96 not possible to store a set of coefficients, only the image processing card 13 ₂ can not perform data conversion processing. Accordingly, the image processing cards 13 do not have _one user, only the image processing card 13 _2, it is possible to prevent the illegally performs data conversion processing.

Then, Now, as image quality improvement, for example, focusing on improving the spatial resolution of the image, the image processing card 13 _1, for example, as shown in FIG. 28, and the extremely high quality image data training data At the same time, learning is performed as student data using low-quality image data obtained by further reducing the spatial resolution of the high-quality image data whose spatial resolution has been reduced, and tap coefficients of 64 classes for super-resolution improvement for greatly improving the resolution are provided. A set is generated, and a set of tap coefficients for the 64 classes is compressed to generate a set of coefficient seed data for 25 taps for super-resolution improvement. It can be stored as generation information.

The image processing card 13 _1, in addition to the 25 sets of coefficient seed data of taps for super resolution enhancement in FIG. 28, for example, as shown in FIG. 29, as well as a high-quality image data and the teacher data Learning is performed using the low-quality image data whose spatial resolution has been reduced as student data, a set of tap coefficients for 64 classes for normal resolution improvement for improving the resolution to some extent, and taps for the 64 classes are further generated. The set of coefficients is compressed to generate a set of coefficient seed data for 25 taps for normal resolution improvement, and the set of coefficient seed data for 25 taps can be stored as generation information.

When only the image processing card 13 ₁ is attached to the image processing interface 40, the image processing card 13 ₁ converts the coefficient seed data for 25 taps for normal resolution improvement into 64 classes for normal resolution improvement. When the image processing cards 13 ₁ and 13 ₂ are attached to the image processing interface 40, the tap conversion is performed using the tap coefficients. It is possible to generate tap coefficients for 64 classes for super-resolution improvement from the coefficient seed data of each minute, and to perform data conversion processing using the tap coefficients.

In this case, in the image processing card 13 ₁ , even if only the image processing card 13 ₁ is mounted or the image processing cards 13 ₁ and 13 ₂ are mounted, the data conversion processing for improving the same type of image quality called resolution improvement is performed. Done. However, when only the image processing card 13 ₁ is mounted, tap coefficients for 64 classes for improving resolution are usually used, whereas when the image processing cards 13 ₁ and 13 ₂ are mounted, Since tap coefficients for 64 classes for super resolution improvement, which are different from tap coefficients for 64 classes for normal resolution improvement, are used, the spatial resolution of image data obtained as a result of data conversion processing can be further improved. it can.

By the way, in the present embodiment, the generation of the tap coefficient w _n from the coefficient seed data β _{m, n} is performed by the tap operation expression of Expression (9).

Therefore, as the image quality improvement, as in FIGS. 28 and 29, focusing on improving the spatial resolution of the image, the image processing card 13 _1, for example, as shown in FIG. 30, the high-quality image data Is used as teacher data, low-quality image data whose spatial resolution has been reduced is learned as student data, and a set of tap coefficients for 64 classes for super-resolution improvement for improving the resolution is generated. A set of tap coefficients for 64 classes is compressed to generate a set of coefficient seed data for 25 taps for super-resolution improvement, and the set of coefficient seed data for 25 taps is stored as generation information. Can be. However, in this case, it is assumed that an M'-th order equation having M 'terms is used as the tap operation expression in Expression (9) when the tap coefficient is compressed into coefficient seed data. Therefore, in this case, a set of coefficient seed data for one tap is composed of M ′ coefficient seed data.

The image processing card 13 _1, in addition to the set of coefficient seed data of 25 taps for resolution enhancement in FIG. 30, for example, as shown in FIG. 31, as well as a high-quality image data and the teacher data, Learning is performed using the low-quality image data whose spatial resolution has been reduced as student data, a set of tap coefficients for 64 classes for improving the resolution for improving the resolution is generated, and a set of tap coefficients for the 64 classes is further generated. Can be compressed to generate a set of coefficient seed data for 25 taps for improving resolution, and this set of coefficient seed data for 25 taps can be stored as generation information. However, in this case, as the tap operation expression of Expression (9) when the tap coefficient is compressed into coefficient seed data, the number of terms is M ″ M ″, which is larger than M ′ in FIG. Shall be used. Therefore, in this case, a set of coefficient seed data for one tap is composed of M ″ coefficient seed data more than M ′.

Then, in the image processing card 13 ₁ , when only the image processing card 13 ₁ is attached to the image processing interface 40, M ′ number of coefficient seed data for 25 taps for resolution improvement for 25 taps per tap are obtained. , it generates a tap coefficient of 64 class component for resolution enhancement, using the tap coefficients, performs data conversion processing, when the image processing card 13 ₁ and 13 ₂ has been attached to the image processing interface 40, From M ″ pieces of coefficient seed data for 25 taps for resolution improvement per tap, tap coefficients for 64 classes for resolution improvement are generated, and data conversion processing is performed using the tap coefficients. can do.

In this case, in the image processing card 13 ₁ , even if only the image processing card 13 ₁ is mounted or the image processing cards 13 ₁ and 13 ₂ are mounted, the data conversion processing for improving the same type of image quality called resolution improvement is performed. Done. However, when only the image processing card 13 ₁ is inserted, the number of terms used in the data conversion process is calculated from M ′ pieces of coefficient seed data for 25 taps for improving resolution and 25 ′ taps per tap. respect but being produced by M 'taps calculation equation (9), when the image processing card 13 ₁ and 13 ₂ is mounted, the tap coefficients used in the data conversion process, per one tap Since the M ″ number of coefficient seed data for 25 taps for resolution improvement are generated by M ″ number of tap arithmetic expressions (9) having more terms than M ′, the tap coefficients can be more accurately calculated. Can be restored. Therefore, as in the case of FIGS. 28 and 29, in the image processing card 13 ₁ , the case where the image processing cards 13 ₁ and 13 ₂ are mounted is compared with the case where only the image processing card 13 ₁ is mounted. As a result, the spatial resolution of the image data obtained as a result of the data conversion process can be further improved.

  Next, FIG. 32 shows a second configuration example of the image processing interface 40 of FIG. In the figure, portions corresponding to those in FIG. 5 are denoted by the same reference numerals, and description thereof will be omitted below as appropriate.

  In the embodiment shown in FIG. 32, the image processing interface 40 reads out generation information from an image processing card 13 shown in FIG. 33, which will be described later, and generates tap coefficients. Then, the image processing interface 40 performs data conversion processing on the image data stored in the frame memory 35 (FIG. 3) using the tap coefficients.

  That is, the first image data read from the frame memory 35 and subjected to the data conversion processing is supplied from the memory interface 82 to the tap extracting units 131 and 132.

  Similar to the tap extracting unit 91 in FIG. 6, the tap extracting unit 131 sequentially sets pixels constituting the second image data as a target pixel, and further uses a first pixel used for predicting a pixel value of the target pixel. Some of (the pixel values of) the pixels constituting the image data are extracted as prediction taps.

  The tap extracting unit 132, like the tap extracting unit 92 in FIG. 6, configures the pixel constituting the first image data used to perform the class classification of classifying the pixel of interest into one of several classes. Are extracted as class taps.

  A control signal is supplied from the interface controller 81 to the tap extracting units 131 and 132, and the tap structure of the prediction tap configured by the tap extracting unit 131 and the tap extracting unit 132 The tap structure of the class tap to be set is set according to a control signal from the interface controller 81. That is, the interface controller 81 reads out processing information from the image processing card 13 of FIG. 33 described later, which is attached to the image processing interface 40, via the card interface 83, and based on the processing information, tap extracting sections 131 and 132 Is controlled.

  The prediction tap obtained by the tap extraction unit 131 is supplied to the prediction unit 135, and the class tap obtained by the tap extraction unit 132 is supplied to the interface controller 81.

  The class classification unit 133 classifies the pixel of interest based on the information supplied from the interface controller 81, and supplies a class code corresponding to the obtained class to the coefficient memory 134. That is, the interface controller 81 supplies the class tap for the target pixel supplied from the tap extracting unit 132 to the image processing card 13 shown in FIG. Then, it requests a class classification for the pixel of interest. Then, in response to the request, the interface controller 81 receives, via the card interface 83, the class code returned by the image processing card 13 after performing the class classification, and supplies the class code to the class classification unit 133. The class classification unit 133 synthesizes the class code as the one or more class classification results of the image processing card 13 attached to the image processing interface 40 supplied from the interface controller 81 in this way, and A class code as a classification result is obtained and supplied to the coefficient memory 134.

  The class classification method in the image processing card 13 is specified by the interface controller 81 based on the processing information read from the image processing card 13 mounted on the image processing interface 40, for example.

  The coefficient memory 134 stores the tap coefficients for each class supplied from the coefficient generation unit 136, and further stores the tap coefficients among the stored tap coefficients at the addresses corresponding to the class codes supplied from the class classification unit 133. The supplied tap coefficient (the tap coefficient of the class represented by the class code supplied from the class classification unit 133) is supplied to the prediction unit 135.

  The prediction unit 135 acquires the prediction tap output from the tap extraction unit 131 and the tap coefficient output from the coefficient memory 134, similarly to the prediction unit 95 in FIG. 6, and uses the prediction tap and the tap coefficient to obtain The prediction calculation of Expression (1) for obtaining the predicted value of the true value of the target pixel is performed. As a result, the prediction unit 135 calculates and outputs the pixel value of the pixel of interest (predicted value thereof), that is, the pixel value of the pixel configuring the second image data.

  The coefficient generation unit 136 receives the generation information read from the image processing card 13 attached to the image processing interface 40 by the interface controller 81 via the card interface 83, and generates a tap coefficient for each class from the generation information. . The tap coefficients for each class are supplied to the coefficient memory 134 and stored.

  Next, FIG. 33 shows a configuration example of the image processing card 13 when the image processing interface 40 is configured as shown in FIG. In the figure, portions corresponding to those in FIG. 6 are denoted by the same reference numerals, and the description thereof will be appropriately omitted below. That is, the image processing card 13 in FIG. 33 includes the class classification unit 93, the generation information storage unit 97, and the card controller 98 in FIG.

  Next, the processing of the image processing interface 40 of FIG. 32 will be described with reference to the flowchart of FIG.

  In the image processing interface 40, in steps S71 to S74, the same processes as those in steps S1 to S4 in FIG. 7 are performed.

Then, in step S74, when the interface controller 81 determines the image processing card 13 _i as a valid card, the process proceeds to step S75, where the interface controller 81 transmits the card interface 83 from all the image processing cards 13 _i as valid cards. The generated information is read out via this. That is, the interface controller 81 via the card interface 83, the image processing as a valid card card 13 _i of the card controller 98 (FIG. 33), requesting the generation information. In this case, the card controller 98 reads out the generation information from the generation information storage unit 97 and supplies it to the interface controller 81 via the card interface 83. Thus, the interface controller 81 to obtain all the generation information of the image processing cards 13 _i as an effective card.

Therefore, the image processing interface 40, for example, in the case where the image processing card 13 ₁ through 13 ₃ is mounted, since all the image processing cards 13 ₁ through 13 ₃ are valid card, interface controller 81, image processing receiving a respective product information card 13 ₁ through 13 _3. When image processing cards 13 ₁ , 13 ₂ , and 13 ₄ are attached to the image processing interface 40, for example, the image processing cards 13 ₁ and 13 ₂ are valid cards. , The generation information of each of the image processing cards 13 ₁ and 13 ₂ is received.

Thereafter, the process proceeds to step S76, the interface controller 81, in the processing information read from the image processing card 13 _i as an effective card, read out from the maximum of the card ID image processing card 13 i _(max) processing information ( based on the maximum ID process information), recognizes the contents of the data conversion process to be performed, in accordance with the recognition result, and controls the image processing cards 13 _i as tap extracting unit 131 and 132, the classification unit 133, and the activation card .

  That is, the interface controller 81 sets the operation mode of the tap extraction unit 131 or 132 so as to constitute, for example, a prediction tap or a class tap having a tap structure described in the maximum ID processing information.

Further, the interface controller 81 sets the operation mode of the class classification unit 133 so that the class code as an effective card is synthesized by the synthesis method described in the maximum ID processing information. That is, in the present embodiment, the processing information of the image processing card 13 _i includes the class code synthesis method (for example, the lower bits of the class code obtained by the image processing card 13 ₁ are obtained by the image processing card 13 _2). In addition, the interface controller 81 sets the operation mode of the class classification unit 133 so as to synthesize the class code according to the information. I do.

Further, the interface controller 81 sets the operation mode of each image processing card 13 _i as an effective card via the card interface 83 so as to perform the class classification according to the class classification method described in the maximum ID processing information. . Namely, in this embodiment, the processing information of the image processing cards 13 _i, class classification method of classification to be performed by the image processing card 131 _to the 13 _i each classifying unit 93 is described, the interface controller 81, the classification method to perform classification by, sets the operation mode of the classification of each image processing card 13 _i as an effective card 93 (FIG. 33).

Then, the process proceeds to step S77, where the interface controller 81 supplies the generation information read from the image processing card 13 _i as a valid card to the coefficient generation unit 136, whereby the tap coefficient for each class is obtained from the generation information. Generate.

  After generating the tap coefficient for each class, the coefficient generation unit 136 proceeds from step S77 to S78, performs a normalization process for level adjustment on the tap coefficient for each class, and supplies the tap coefficient to the coefficient memory 134 for storage. .

  Then, the process proceeds to step S79, in which the tap extracting unit 131 uses the image data supplied from the memory interface 82 and stored in the frame memory 35 (FIG. 3) as the first image data to be subjected to the data conversion processing. Each pixel constituting the second image data (image data after data conversion processing) for one image data is sequentially set as a target pixel, and the target pixel is set as a prediction tap having the tap structure set in step S76. The pixels of the first image data are extracted. Further, in step S79, the tap extracting unit 132 extracts, from the pixel of interest, pixels of the first image data that are to be class taps having the tap structure set in step S76. Then, the prediction tap is supplied from the tap extraction unit 131 to the prediction unit 135, and the class tap is supplied from the tap extraction unit 132 to the interface controller 81.

Upon receiving the class tap supplied from the tap extracting unit 132, the interface controller 81 controls the card interface 83 in step S80 to extract the taps from the image processing cards _13i that are valid cards. The class tap is supplied from the unit 132 to request the class classification.

Here, each of the image processing cards 13 _i that are valid cards responds to a request from the interface controller 81 based on a class tap from the interface controller 81, as described later with reference to FIG. The pixels are classified into classes, and a class code as a result of the classification is supplied to the interface controller 81 via the card interface 83.

Thereafter, the process proceeds to step S81, the interface controller 81, all of the image processing cards 13 _i that are enabled card, determines whether it has received a class code as the classification result.

In step S81, if all of the image processing cards 13 _i that are enabled card, the class code as the classification result, is determined not yet received, the flow returns to step S81.

If it is determined in step S81 that the class codes as the classification results have been received from all the image processing cards 13 _i that are valid cards, all the class codes are supplied to the classification unit 133. Then, the process proceeds to step S82.

In step S82, the class classification unit 133 combines the class codes supplied from the interface controller 81 from the respective image processing cards _13i that are valid cards, thereby obtaining the final class classification result of the pixel of interest. Is obtained and supplied to the coefficient memory 134, and the process proceeds to step S83.

  In step S83, the coefficient memory 134 reads and outputs the tap coefficient stored at the address corresponding to the class code supplied from the class classification unit 133, that is, the tap coefficient of the class corresponding to the class code. Further, in step S83, the prediction unit 135 acquires the tap coefficients output from the coefficient memory 134, and proceeds to step S84.

  In step S84, the prediction unit 135 performs the prediction calculation of Expression (1) using the prediction tap output from the tap extraction unit 131 and the tap coefficient acquired from the coefficient memory 134. Accordingly, the prediction unit 135 obtains the pixel value of the target pixel and supplies the pixel value to the line-sequential conversion unit 85. Then, the process returns to step S71, and the same processing is repeated thereafter.

Next, with reference to the flowchart of FIG. 35 describes the processing of the image processing cards 13 _i which enabled card.

In the image processing card 13 _{i that} has become an effective card, first, in step S91, the card controller 98 determines whether or not there has been a request for class classification together with a class tap from the (interface controller 81 of) the image processing interface 40. If not, the process returns to step S91.

  If it is determined in step S91 that a request for class classification has been made, the process proceeds to step S92, and the card controller 98 receives the class tap request and the class tap supplied from the image processing interface 40 together with the request for class classification. It requests the classifying unit 93 to perform class classification based on the class tap. Thereby, the class classification unit 93 classifies the target pixel based on the class tap according to the class classification method set in step S76 of FIG. 34, and supplies the resulting class code to the card controller 98. .

  Then, the process proceeds to step S93, and the card controller 98 transmits the class code from the class classification unit 93 to the image processing interface 40. Then, the process returns to step S91, and thereafter, the same processing is repeated.

Next, the class classification in the class classification unit 93 (FIG. 33) of each of the image processing cards 13 _i which are regarded as valid cards and the class classification unit 133 (FIG. The process 32) will be further described.

  Here, it is assumed that the tap extracting unit 132 configures, for the target pixel, for example, a class tap including a total of 9 pixels of 3 × 3 pixels.

For example, assuming that the image processing cards 13 _{1 to} 13 ₃ are attached to the image processing interface 40 and are valid cards, the class classification unit 93 (FIG. 33) of the image processing card 13 _i will Classification is performed by performing 1-bit ADRC processing, thereby outputting a 9-bit class code.

The image processing card 13 ₂ of the classification unit 93 (FIG. 33), for example, to detect the motion vector of the center pixel of the class tap (center pixel), the size of the motion vector, with a predetermined threshold value comparison By doing so, the presence or absence of movement of the center pixel is determined, and a 1-bit class code representing the presence or absence of the movement is output. In this case, the image processing card 13 ₂ of the classification unit 93, for example, stores the previous frame of the image data supplied as class taps to the previous frame, and the preceding frame image data, A motion vector is detected by performing block matching using a class tap. Then, the classification unit 93 of the image processing cards 13 ₂ judges, for example, when the size of the motion vector is greater than a predetermined threshold, determines that there is motion, otherwise, the no motion .

Further, the image processing cards 13 ₃ of the classification unit 93 (FIG. 33), for example, calculates the center pixel of the class tap, a difference between pixel adjacent thereto, comparing the absolute value of the difference with a predetermined threshold value , The presence or absence of an edge in the center pixel is determined, and a 1-bit class code representing the presence or absence of the edge is output. In this case, the image processing cards 13 ₃ of the classification unit 93, for example, the center pixel, in the absolute value of the difference of the pixel adjacent thereto, in the presence of those than a predetermined threshold Dai, It is determined that there is an edge; otherwise, it is determined that there is no edge.

Here, hereinafter, the classification performed by the classifying unit 93 of the image processing cards 13 _i, that i-th classification. A class code obtained as a result of the i-th class classification is called an i-th class code.

In this case, the class classifying unit 133 of the image processing interface 40 (FIG. 32), for example, as the lower bits of the first class code obtained by the first classification of the image processing cards 13 ₁ classification unit 93, an image processing card 13 adds a second class code obtained by the second classification of the _second classification section 93, further, as its lower bits, the image processing cards 13 third obtained by the third classification of the _third classification section 93 Add class code.

That is, for example, the image processing interface 40, when the image processing card 13 ₁ only not mounted, the classification unit 133 (FIG. 32) of the image processing interface 40, the image processing card 13 _first classifying unit 93 The 9-bit first class code obtained by the first class classification is directly output as a final class code (hereinafter, appropriately referred to as a final class code).

Further, for example, the image processing interface 40, when the image processing card 13 ₁ and 13 ₂ is mounted, the classification unit 133 (FIG. 32) of the image processing interface 40, image processing card 13 ₁ classification on the low-order bits of the 9 bits of the first class code obtained by the first classification of the part 93 adds the second class code of 1 bit obtained by the second classification of the classification unit 93 of the image processing cards 13 ₂ , And outputs the resulting 10 bits as the final class code.

Furthermore, for example, the image processing interface 40, when the image processing card 13 ₁ through 13 ₃ is mounted, the classification unit 133 (FIG. 32) of the image processing interface 40, image processing card 13 ₁ classification on the low-order bits of the 9 bits of the first class code obtained by the first classification of the part 93 adds the second class code of 1 bit obtained by the second classification of the classification unit 93 of the image processing cards 13 ₂ further adding a third class code of 1 bit obtained by the third classification of the image processing cards 13 ₃ of the classification unit 93, the 11 bits obtained as a result is output as a final class code.

As described above, when mounting the image processing card 131 _to 13 _i, a final class code class classification section 133 is outputted, the image processing card 131 _to the 13 _i-1 is installed, the class classification section 133 outputs When the number of classes is increased by one bit from the final class code, as shown in FIG. 36, the number of classes varies depending on the number of image processing cards 13 _i as valid cards attached to the image processing interface 40.

That is, the image processing interface 40, when the image processing card 13 ₁ only is not mounted, the final class code becomes 9 bits, the number of the class, 512 (= 2 ⁹⁾ class become (leftmost in FIG. 36 Field). When the image processing cards 13 ₁ and 13 ₂ are attached to the image processing interface 40, the final class code is 10 bits, and the number of classes is 1024 (= 2 ¹⁰ ) classes (see FIG. 36, second column from the left). Furthermore, when the image processing cards 13 _{1 to} 13 ₃ are attached to the image processing interface 40, the final class code is 11 bits, and the number of classes is 2048 (= 2 ¹¹ ) classes (FIG. 36). To the right).

Therefore, the image processing card 13 _1, for example, in the learning apparatus of FIG. 11, as a class classification method classification unit 117, adopted classification for outputting a first class code obtained by the first classification Learning is performed, a set of tap coefficients according to the first class for 512 classes is generated, and a set of coefficient seed data according to the first class generated by compressing the set of tap coefficients for the 512 classes is: It is stored as generation information.

Further, the image processing card 13 _2, for example, in the learning apparatus of FIG. 11, as a class classification method classifying unit 117, obtained by the second classification to the lower bits of the first class code obtained by the first classification Learning is performed by employing a class classification that outputs a class code to which a second class code is added, and a set of tap coefficients is generated for the 1024 classes by the first class classification and the second class classification. A set of coefficient seed data according to the first class classification and the second class classification generated by compressing the set of minute tap coefficients is stored as generation information.

In this case, the image processing interface 40, when only the image processing card 13 ₁ is mounted, the image processing interface 40, as shown in FIG. 37, stored in the image processing card 13 ₁ of the highest order among the effective card The set of coefficient seed data according to the first class classification as the generated information is read out, and the coefficient generation unit 136 generates a set of tap coefficients according to the first class classification for 512 classes from the set of coefficient seed data. You. Further, in the image processing card 13 _first classifying unit 93, by first classification is performed, 9 first class code bits are outputted, the image processing interface 40, the classification unit 133, a first The class code is output as it is as the final class code. In the image processing card 13 _1, 512 of the set of tap coefficients by class partial first class classification, the set of tap coefficients corresponding to the first class code of 9 bits, the data conversion processing (prediction section 135 ( 32)).

Further, the image processing interface 40, when the image processing card 13 ₁ and 13 ₂ is mounted, the image processing interface 40, as shown in FIG. 38, the lowest rank of the image processing cards 13 ₂ in the memory of the valid cards The set of coefficient seed data according to the first class classification and the second class classification as the generated generation information is read out, and the coefficient generation unit 136 divides the set of coefficient seed data from the set of coefficient seed data into 1024 classes of the first class classification and the second class classification. A set of tap coefficients according to the two-class classification is generated. Further, in the image processing card 13 _first classifying unit 93, by first classification is performed, 9 first class code bits are outputted, the image processing card 13 ₂ of the classification unit 93, the second class By performing the classification, a 1-bit second class code is output. In the image processing interface 40, the class classifying unit 133 generates and outputs a 10-bit final class code by adding the second class code to the lower bits of the first class code. Further, in the image processing interface 40, a set of tap coefficients corresponding to a 10-bit final class code among a set of tap coefficients according to the first class classification and the second class classification for 1024 classes is used for data conversion processing. Can be

Accordingly, in this case, as the number of the image processing cards 13 _i that are mounted on the image processing card 40 and are valid cards increases, the data conversion process is performed using the tap coefficient having the larger number of classes. The degree will be improved.

  That is, for example, even if learning of a set of tap coefficients by the first class classification and the second class classification and learning of a set of tap coefficients by the first class classification are performed using the same learning pair, the first class Since the set of tap coefficients according to the classification and the second class classification has a larger number of classes than the set of tap coefficients according to the first class classification, the data conversion processing is performed using the set of tap coefficients according to the first class classification and the second class classification. Is performed, the degree of image quality improvement is improved compared to the case where data conversion processing is performed using a set of tap coefficients according to the first class classification.

The image processing cards 13 ₁ and 13 ₂ may store coefficient seed data for improving image quality of the same type, or may store coefficient seed data for improving image quality of different types. That is, for example, it is possible to store coefficient seed data for noise removal in the image processing card 13 ₁ and to store coefficient seed data for improving the spatial resolution in the image processing card 13 _2. is there. Furthermore, in this case, in the image processing interface 40 in FIG. 32, the frame memory 35 to the target image data stored in (FIG. 3), the tap coefficient seed data stored in the image processing card 13 ₁ is generated coefficients performs data conversion processing using a result image data obtained by the target, is possible to perform the data conversion process by using the tap coefficients generated from the coefficient seed data stored in the image processing data 13 ₂ It is possible.

In addition, in the lower image processing card 13 ₂ , in addition to the image quality improvement by the coefficient type data stored in the upper image processing card 13 ₁ , coefficient type data for performing other types of image quality improvement should be stored. Is also possible. That is, for example, the image processing card 13 ₁ stores coefficient seed data for removing noise, and the image processing card 13 ₂ stores coefficient seed data for removing noise and improving spatial resolution. It is possible to keep. In this embodiment, the number of class taps coefficients generated from the coefficient seed data of the image processing cards 13 ₂ to 2 times the number of class taps coefficients generated from the coefficient seed data of the image processing cards 13 ₁ from becoming, the degree of noise reduction by the tap coefficients generated from the coefficient seed data of the image processing cards 13 _2, by the tap coefficients generated from the coefficient seed data of the image processing cards 13 ₁ to the same extent as the degree of noise reduction It is considered to be. Therefore, when the image processing cards 13 ₁ and 13 ₂ are mounted, the same level of noise removal is performed as when only the image processing card 13 ₁ is mounted, and the resolution is further improved.

Similar tap coefficients can be stored in the image processing cards 13 _i other than the image processing cards 13 ₁ and 13 _{2. In} this case, the lower order image processing is performed sequentially from the top image processing card 13 _1. by going to seat the card 13 _i, since the type of improved image quality will be increases, i.e., the function of the main body 1 of the television receiver, so continue to add to higher functionality, An incentive for the user to purchase the image processing card 13 is likely to work.

Incidentally, in the above-described case also, the image processing cards 13 _i other than the image processing card 13 ₁ the uppermost, as described with reference to FIG. 19, instead of the coefficient seed data itself, as previously stores the difference data It is possible to do.

Next, in the above case, the coefficient seed data or the difference data from the coefficient seed data used in the next higher image processing card 13 _i-1 is stored in the image processing card 13 _i of FIG. It was to be stored as, in the image processing cards 13 _i excluding the image processing card 13 ₁ the uppermost, from the tap coefficients used in the image processing card 13 _i-1 of the one upper, new taps differs from that Information (information other than difference data) for generating a coefficient can be stored as generation information.

In this case, when the image processing cards 13 ₁ and 13 ₂ are attached to the image processing interface 40, as shown in FIG. 39, the coefficient generation unit 136 (FIG. 32) of the image processing interface 40 from a set of coefficient seed data by the first classification stored in 13 _1, the set of tap coefficients according to the first classification is generated. Moreover, the coefficient generation unit 136 of the image processing interface 40, the a set of tap coefficients according to the first classification, and a generation information stored in the image processing card 13 _2, first classification and the tap of the second class classification A set of coefficients is generated.

  Accordingly, an example of generation information for generating a set of tap coefficients according to the first class classification and the second class classification together with a set of tap coefficients according to the first class classification will be described.

  In the following, when the first class code according to the first class classification is appropriately used as the final class code, the final class code is referred to as a first composite class code, and lower bits of the first class code include When a second class code according to the second class classification is added to form a final class code, the final class code is referred to as a second composite class code. In the following, when the second class code is added to the lower bits of the first class code, and the third class code is further added as a final class code, the final class code is changed to the third class code. It is called composite class code.

  In this case, the second composite class code is obtained by adding a 1-bit second class code to lower bits of the first composite class code, and the third composite class code is a second composite class code. And a 1-bit third class code.

  Therefore, a certain first composite class code #c has a second composite class code # c0 in which 1-bit 0 is added to the lower bits of the first composite class code #c and a 1-bit 1 The second composite class code # c1 can be associated with the second composite class code # c1. Similarly, a certain second composite class code #c ′ is composed of a third composite class code # c′0 in which 1-bit 0 is added to lower bits of the second composite class code #c ′ and 1 It can be associated with two of the third composite class codes # c′1 to which the bit 1 is added.

  Since one set of tap coefficients exists for one class code, as described above, the first combined class code #c corresponds to the two second combined class codes # c0 and # c1. 40, a set of tap coefficients of the first combined class code #c according to the first class classification and a second combined class code # c0 according to the first and second class classifications, as shown in FIG. , # C1 can be associated with sets of tap coefficients.

  In this case, as the generation information, a set of tap coefficients of each of the second combined class codes # c0 and # c1 according to the first and second class classifications is set as the first combined class code #c according to the first class classification. What can be generated with the set of tap coefficients can be employed.

  Therefore, a method of generating the generation information will be described with reference to a flowchart of FIG. 41, as an example of generating a set of tap coefficients of the second composite class code # c0 according to the first and second class classifications. .

  In this case, first, in step S101, the center tap coefficient of the set of tap coefficients of the first combined class code #c and the second combined class code associated with the first combined class code #c The center tap coefficient of the set of tap coefficients of class code # c0 is compared.

  Here, the center tap coefficient of a set of tap coefficients of a certain class means the tap coefficient located at the center when the tap coefficients of the class are arranged in the order of the tap numbers. Therefore, for example, as shown in FIG. 40, when the tap coefficients of the first combined class code #c are arranged in the order of the tap numbers, (0.2, -0.1, 0.8, -0.1, 0.2) The center tap coefficient becomes 0.8. When the tap coefficients of the second composite class code # c0 are arranged in the order of the tap numbers, (0.3, -0.4, 1.2, -0.4, 0.3), the center tap coefficient is 1.2. .

  After the center tap coefficients are compared in step S101, the process proceeds to step S102, where the center tap coefficient of the second combined class code # c0 is larger than the center tap coefficient of the first combined class code #c (or more). ) Is determined.

  If it is determined in step S102 that the center tap coefficient of the second combined class code # c0 is larger than the center tap coefficient of the first combined class code #c, the process proceeds to step S103, and the second combined class For each positive tap coefficient of the set of tap coefficients of code # c0, the rate of change (ratio) of the first combined class code #c to the corresponding tap coefficient is determined. Further, in step S103, an average value of the change rates is obtained and set as an average change rate v.

  Therefore, in the case shown in FIG. 40, the center tap coefficient of the second combined class code # c0 is 1.2, which is larger than 0.8, which is the center tap coefficient of the first combined class code #c. In step S103, the positive tap coefficient of the tap coefficient of the second composite class code # c0, that is, the first tap coefficient of 0.3, the third tap coefficient (center tap coefficient) of 1.2, the fifth tap For each of the 0.3 coefficients, a change rate (ratio) of the first combined class code #c to the corresponding tap coefficient is determined.

  In this case, the first, third, and fifth tap coefficients of the first composite class code #c are 0.2, 0.8, and 0.2, respectively. For the third tap coefficient, 1.2 / 0.8 is obtained as the change rate, and for the third tap coefficient, 0.3 / 0.2 is obtained as the change rate.

  Further, the average value of these change rates is 1.5, and therefore, 1.5 is obtained as the average change rate v.

  After the processing in step S103, the process proceeds to step S104, in which a + sign is added to the average change rate v to make + v, and this + v is calculated from the set of tap coefficients of the first composite class code #c. , Generation information for generating a set of tap coefficients of the second composite class code # c0 associated therewith (generation information for the second composite class code # c0), and the process ends.

  Therefore, in the above example, +1.5 is the generation information for generating the set of tap coefficients of the second combined class code # c0 from the set of tap coefficients of the first combined class code #c.

  On the other hand, if it is determined in step S102 that the center tap coefficient of the second combined class code # c0 is not larger than the center tap coefficient of the first combined class code #c, the process proceeds to step S105, and the second combining For each of the negative tap coefficients of the set of tap coefficients of the class code # c0, the rate of change (ratio) of the first combined class code #c to the corresponding tap coefficient is determined. Further, in step S105, the average value of the change rate is obtained, and is set as the average change rate v.

  After the process of step S105, the process proceeds to step S106, where the average change rate v is set to -v by adding a sign of-, and -v is the tap coefficient of the first combined class code #c. The set is set as generation information for generating a set of tap coefficients of the second composite class code # c0 associated with the set, and the process ends.

  For example, when generating generation information for generating a set of tap coefficients of the second combined class code # c1 based on the first and second class classifications illustrated in FIG. 40, the second combined class Since the set of tap coefficients of the code # c1 is (0.17, -0.02, 0.7, -0.02, 0.17), the center tap coefficient is 0.7, which is the center tap coefficient of the first composite class code #c. It is a value of 0.8 or less.

  Accordingly, in this case, the first synthesis is performed for each of the negative tap coefficients of the tap coefficients of the second synthesis class code # c1, that is, -0.02 which is the second tap coefficient and -0.02 which is the fourth tap coefficient. A change rate (ratio) of the class code #c with respect to the corresponding tap coefficient is obtained (step S105).

  In this case, the second and fourth tap coefficients of the first composite class code #c are -0.1 and -0.1, respectively, so for the second tap coefficient, 0.02 / 0.1 is the fourth tap coefficient. For the tap coefficient, 0.02 / 0.1 is obtained as the change rate.

  Further, the average value of these change rates is 0.2, and thus 0.2 is obtained as the average change rate v (step S105).

  Then, in this case, −0.2, which is obtained by adding a sign of − to 0.2, which is the average change rate v, is obtained from the set of tap coefficients of the first combined class code #c, by the second combined class code # c1. This is set as generation information for generating a set of tap coefficients (generation information for the second composite class code # c1) (step S106).

  Next, referring to the flowchart of FIG. 42, the first and second sets of tap coefficients of the first composite class code #c according to the first class classification and the generation information generated by the processing of FIG. 41 will be described. The process of the coefficient generation unit 136 in FIG. 32 will be described with an example of newly generating a set of tap coefficients of the second composite class code # c0 according to the class classification of FIG.

  In step S111, the coefficient generation unit 136 determines the sign of the generation information for the second composite class code # c0, proceeds to step S112 when it is determined to be +, and proceeds to step S112 when it is determined to be-. The process proceeds to step S114.

  In step S112, the coefficient generation unit 136 converts the average change in the generation information into a positive tap coefficient among the set of tap coefficients of the first composite class code #c associated with the second composite class code # c0. The ratio v is multiplied, and the set of tap coefficients resulting from the multiplication is set as the set of tap coefficients of the second composite class code # c0, and the process proceeds to step S113.

  In step S113, the coefficient generator 136 performs a normalization process for gain adjustment on the set of tap coefficients of the second composite class code # c0 obtained in step S112, and ends the process. That is, in step S113, the coefficient generation unit 136 adjusts the negative tap coefficients in the set of tap coefficients of the second composite class code # c0 obtained in step S112 so that the total sum thereof becomes 1. .

  On the other hand, in step S114, the coefficient generation unit 136 sets the negative tap coefficient in the generation information in the set of tap coefficients of the first composite class code #c associated with the second composite class code # c0. The average coefficient of change v is multiplied, and the set of tap coefficients resulting from the multiplication is set as the set of tap coefficients of the second composite class code # c0, and the process proceeds to step S115.

  In step S115, the coefficient generation unit 136 performs normalization processing for gain adjustment on the set of tap coefficients of the second combined class code # c0 obtained in step S114, and ends the processing. That is, in step S115, the coefficient generation unit 136 adjusts the positive tap coefficients of the set of tap coefficients of the second composite class code # c0 obtained in step S114 so that the total sum thereof becomes 1. .

  According to the processing of FIG. 42, for example, the set of tap coefficients of the first composite class code #c is (0.2, -0.1, 0.8, -0.1, 0.2) as shown in FIG. When the generation information of the second composite class code # c0 is +1.5 as described with reference to FIG. 41, the sign of the generated information is +, so the tap coefficient of the first composite class code #c The positive tap coefficient of the set (0.2, -0.1, 0.8, -0.1, 0.2) is multiplied by 1.5, and the multiplication result (0.3, -0.1, 1.2, -0.1, 0.3) becomes the second The set of tap coefficients of the composite class code # c0 is set (step S112). Further, by adjusting the negative tap coefficients of the set of tap coefficients (0.3, -0.1, 1.2, -0.1, 0.3) of the second composite class code # c0, the total sum becomes 1 It is adjusted to. That is, in this case, since the sum of (0.3, -0.1, 1.2, -0.1, 0.3) is 1.6, the second and fourth tap coefficients that are negative tap coefficients are, for example, -0.1 to -0.1. By adjusting to the same amount to 0.4, the set of tap coefficients of the second composite class code # c0 is set to have a sum of 1 (0.3, -0.4, 1.2, -0.4, 0.3). .

  The set of tap coefficients (0.3, -0.4, 1.2, -0.4, 0.3) of the second composite class code # c0 matches the set of original tap coefficients shown in FIG. From the set of tap coefficients (0.2, -0.1, 0.8, -0.1, 0.2) of the combined class code #c and the generation information +1.5, the first combination class code #c is associated with the first combined class code #c. A set of tap coefficients (0.3, -0.4, 1.2, -0.4, 0.3) of the second composite class code # c0 can be generated. Similarly, a set of tap coefficients of the second composite class code # c1 is generated as described with reference to FIG. 41 and a set of tap coefficients of the first composite class code #c associated therewith. Can be generated from the generated information -0.2.

Here, the image processing cards 13 ₃ to 13 _6, respectively, similarly to the case described above, together with the tap coefficients generated from the generation information of the image processing cards 13 _i of the one upper, to generate a new tap coefficient Generation information can be generated and stored.

  In the embodiment of FIG. 41, the information obtained by adding the average rate of change to the plus or minus sign as it is is generated as the generation information for the second composite class code # c0. The average rate of change is calculated using the second composite class code # c0, the first composite class code #c associated therewith, or the first class used to generate the second composite class code # c0. A correction based on the code or the second class code and the like, and a value obtained by adding a plus or minus sign to the corrected average rate of change can be used as generation information for the second composite class code # c0. It is.

  In the embodiment of FIG. 41, when the center tap coefficient of the second combined class code # c0 is larger than the center tap coefficient of the first combined class code #c, For a positive tap coefficient, an average change rate v is obtained. When the average change rate v is not large, an average change rate v is obtained for a negative tap coefficient of the second composite class code # c0. A minus sign is added to generate generation information (hereinafter, appropriately referred to as first generation information). Conversely, the center tap coefficient of the second composite class code # c0 is changed to the first If it is not larger than the center tap coefficient of the composite class code #c, the average change rate v is calculated for the positive tap coefficient of the second composite class code # c0. If it is larger, the second composite class code # c0 Negative of For the coefficient, an average rate of change v is obtained, and a sign of + or-is added to the average rate of change v to generate generated information (hereinafter, appropriately referred to as second generated information). Is possible.

  That is, when the set of tap coefficients of the second composite class code # c0 is obtained by the processing in FIG. 42 using the generation information on the second composite class code # c0, there may be deviation from the original value. . Therefore, of the first and second generation information, the one in which the amount of deviation (error) of the set of tap coefficients of the second combined class code # c0 obtained from each of them is smaller than the original value is determined by the second It can be adopted as final generation information for the composite class code # c0. It should be noted that which of the first and second generation information is adopted as the final generation information depends on the second composite class code, the first composite class code associated therewith, or the first composite class code. It can be set for each of the first class code and the second class code used to generate the second composite class code.

  The generation information for generating the set of tap coefficients of the second composite class code # c0 associated therewith from the set of tap coefficients of the first composite class code #c is the processing of FIG. For example, in the learning device of FIG. 9, a set of tap coefficients of the first combined class code #c is used as student data, and a set of tap coefficients of the second combined class code # c0 is Learning can be performed as student data, and a set of tap coefficients obtained as a result can be employed.

  Next, FIG. 43 shows a third configuration example of the image processing interface 40 of FIG. In the figure, portions corresponding to the case in FIG. 32 are denoted by the same reference numerals, and the description thereof will be appropriately omitted below. That is, the image processing interface 40 of FIG. 43 has the same configuration as that of FIG. 32 except that a memory space sharing control unit 141 is provided instead of the coefficient memory 134.

  The memory space sharing control unit 141 targets a coefficient memory 94 of the image processing card 13 of FIG. 44, which will be described later, attached to the image processing interface 40, and operates similarly to the memory space sharing control unit 100 of FIG. A virtual memory space for storing a set of tap coefficients generated by the 136 is secured, a set of tap coefficients is stored in the virtual memory space, and a set of tap coefficients of a class code output by the class classification unit 133 is The data is read from the virtual memory space and supplied to the prediction unit 135.

  That is, FIG. 44 shows a configuration example of the image processing card 13 when the image processing interface 40 is configured as shown in FIG. In the figure, portions corresponding to those in FIG. 33 are denoted by the same reference numerals, and description thereof will be omitted as appropriate below. That is, the image processing card 13 in FIG. 44 is configured by providing the coefficient memory 94 shown in FIG. 20 with respect to the image processing card 13 in FIG.

As described in FIG. 36, is mounted to the image processing interface 40, the the image processing card 131 _to 13 _i as an effective card is increased, the tap coefficients generated by the coefficient generation unit 136 of the image processing interface 40 Class The number increases, and so does the size of the entire set of tap coefficients.

In the embodiment of Figure 44, if the image processing card 131 _to 13 _i is attached, the sum of the capacity of the image processing cards 13 ₁ through 13 _i each coefficient memory 94, the coefficient of the image processing interface 40 generates to match the set overall size of the tap coefficients generated by the section 136, the capacity of the coefficient memory 94 of the image processing cards 13 _i are designed, therefore, the stored contents of the image processing cards 13 ₁ through 13 _i It reads illegally, and generate a set of tap coefficients, as long as the image processing card 131 _to 13 _i is not mounted to the image processing interface 40, so it is impossible to store a set of the tap coefficients I have. In this case, the user reads out the stored contents of the image processing cards 13 ₁ through 13 _i illegally, and produces a set of tap coefficients can be prevented from being used.

Here, the memory space sharing control unit 141 of FIG. 43 is attached to the image processing interface 40 when the memory space sharing control unit 141 is viewed from the class classification unit 133, the coefficient generation unit 136, and the prediction unit 135. A virtual memory space is secured so that the real memory space of the i coefficient memories 94 of the image processing cards 13 _{1 to} 13 _i as valid cards can be viewed as one continuous memory space as a whole.

Also, as in the case described above, the sum of the capacity of the coefficient memory 94 of the image processing is attached to the image processing interface 40 cards 13 ₁ through 13 _i coincides with the size of the tap coefficients is generated, the coefficient memory 94 of was to design the capacity, the capacity of the coefficient memory 94, the image processing card 131 _to the size of the tap coefficient 13 _i is generated when the mounted, i-1 one volume of the coefficient memory 94 It is possible to set the value to be larger and equal to or smaller than the capacity of the i coefficient memories 94.

In the embodiment of FIGS. 33 and 44, each image processing card 13 _i, has been to provide a classification unit 93, the classification unit 93 provided in the respective image processing card 13 _i, each image processing card 13 instead _i, it is possible to be provided to the image processing interface 40 of FIG. 32 or FIG. 43.

  Next, the series of processes described above can be performed by hardware or can be performed by software. When a series of processing is performed by software, a program constituting the software is installed in a general-purpose computer or the like.

  Therefore, FIG. 45 illustrates a configuration example of an embodiment of a computer in which a program for executing the above-described series of processes is installed.

  The program can be recorded in advance on a hard disk 305 or a ROM 303 as a recording medium built in the computer.

  Alternatively, the program may be temporarily or temporarily stored in a removable recording medium 311 such as a flexible disk, a CD-ROM (Compact Disc Read Only Memory), an MO (Magneto Optical) disk, a DVD (Digital Versatile Disc), a magnetic disk, and a semiconductor memory. It can be stored (recorded) permanently. Such a removable recording medium 311 can be provided as so-called package software.

  The program may be installed in the computer from the removable recording medium 311 as described above, or may be wirelessly transferred from the download site to the computer via a digital satellite broadcasting artificial satellite, or may be connected to a LAN (Local Area Network), The program can be transferred to a computer via a network such as the Internet by wire, and the computer can receive the transferred program by the communication unit 308 and install the program on the built-in hard disk 305.

  The computer includes a CPU (Central Processing Unit) 302. An input / output interface 310 is connected to the CPU 302 via a bus 301. The CPU 302 operates the input unit 307 including a keyboard, a mouse, and a microphone by the user via the input / output interface 310. When a command is input by the above operations, the program stored in the ROM (Read Only Memory) 303 is executed in accordance with the command. Alternatively, the CPU 302 may execute a program stored in the hard disk 305, a program transferred from a satellite or a network, received by the communication unit 308 and installed in the hard disk 305, or a removable recording medium 311 attached to the drive 309. The program read and installed in the hard disk 305 is loaded into a RAM (Random Access Memory) 304 and executed. Accordingly, the CPU 302 performs the processing according to the above-described flowchart or the processing performed by the configuration of the above-described block diagram. Then, the CPU 302 outputs the processing result from the output unit 306 composed of an LCD (Liquid CryStal Display) or a speaker, for example, via the input / output interface 310, or the communication unit 308 as necessary. The data is transmitted and further recorded on the hard disk 305.

  Here, in the present specification, processing steps for describing a program for causing a computer to perform various processes do not necessarily need to be processed in a time series in the order described as a flowchart, and may be performed in parallel or individually. The processing to be executed (for example, parallel processing or processing by an object) is also included.

  Further, the program may be processed by one computer, or may be processed in a distributed manner by a plurality of computers. Further, the program may be transferred to a remote computer and executed.

  In this embodiment, the case where the present invention is applied to the improvement of the image quality of image data has been described. However, the present invention can be applied to the improvement of the sound quality of audio (sound) data.

  Further, in the present embodiment, the case where the present invention is applied to an analog television receiver has been described. However, the present invention is applicable to, for example, a digital television receiver and other image data and audio data. The present invention can be applied to a VTR (Video Tape Recorder) for processing and other devices.

  Further, in the present embodiment, in particular, the generation information for generating the tap coefficients for removing noise and improving the spatial resolution is stored in the image processing card 13. The information is not limited to these. That is, the image processing card 13 includes, for example, a tap coefficient for improving the time resolution, a tap coefficient for improving the gradation of the pixel (the number of bits of the pixel value), a tap coefficient for enhancing the edge, and the number of pixels constituting the image. And generation information for generating a tap coefficient for converting the size of an image and other tap coefficients having an effect of improving image quality.

Furthermore, the generation information stored in each of the image processing cards 13 _{1 to} 13 ₆ may be the same type of image quality improvement, and may be a type of generating a tap coefficient that realizes a different degree of image quality improvement. A tap coefficient to be realized may be generated.

In the present embodiment, the generation information is stored in the image processing card 13 in advance. However, the image processing cards 13 _i of each rank can be sold without storing the generation information. It is. In this case, the generation information can be downloaded from a generation information server that provides the generation information and stored in the image processing card _13i . That is, when the image processing card 13 _i is mounted on the image processing interface 40, the controller 37 in FIG. 3 controls the communication interface 38 to access the generation information server and perform mutual authentication. The generated information can be downloaded from the generated information server. In this case, compensation for the download of the generated information, in response to the download, may be debited from the bank account of the user or the like, in advance, may have been included in the selling price of the image processing card 13 _i .

The lower the image processing card 13 is attached, the more advanced the function of the main body 1 becomes. Therefore, the selling price of the image processing card 13 _i or the price for downloading the generated information is calculated as follows. as the order of processing card 13 _i is lowered, it can be a high price.

Further, in the present embodiment, the television receiver, in advance, the image processing card 13 of _the best rank is assumed that in a state of being mounted, the image processing card 13 ₁ of the highest order are also other as with the image processing card 13 _i, the television receiver can be sold separately. However, first, for the user to image processing card 13 ₁ of the highest order has purchased the television receiver of the state, which is mounted, in a state in which image processing card 13 ₁ of the highest order has not been fitted with a television receiver than the user who has purchased, it is possible to make the image processing card 13 _i selling price of the lower of the order to lower prices.

  In addition, the main body 1 of the television receiver shown in FIG. 3 roughly includes, for example, adding an image processing interface 40 to a general analog television receiver and changing a program to be executed by the controller 37. It is possible to configure by doing. Accordingly, the main body of the television receiver shown in FIG. 3 can be manufactured relatively easily using a general analog television receiver, and is provided by mounting the image processing card 13. Considering the functions described above, the cost merit (cost performance) can be said to be high.

FIG. 1 is a perspective view illustrating a configuration example of an embodiment of a television receiver to which the present invention has been applied. FIG. 3 is a rear view of the main body 1. FIG. 2 is a block diagram illustrating an example of an electrical configuration of a main body 1. FIG. 3 is a plan view showing a configuration example of a remote controller 2. FIG. 2 is a block diagram illustrating a first configuration example of an image processing interface 40. FIG. 2 is a block diagram illustrating a first configuration example of an image processing card 13. 5 is a flowchart illustrating processing of an image processing interface. 5 is a flowchart illustrating a process of the image processing card. FIG. 3 is a block diagram illustrating a configuration example of a learning device that calculates tap coefficients. It is a flowchart explaining the learning process which calculates | requires a tap coefficient. FIG. 3 is a block diagram illustrating a configuration example of a learning device that obtains coefficient seed data. It is a flowchart explaining the learning process which calculates | requires coefficient seed data. FIG. 6 is a diagram showing tap coefficients and coefficient seed data generated from student data and teacher data. FIG. 4 is a diagram illustrating a method of generating coefficient seed data stored in an image processing card 13. FIG. 4 is a diagram illustrating a method of generating coefficient seed data stored in an image processing card 13. FIG. 4 is a diagram illustrating a method of generating coefficient seed data stored in an image processing card 13. Is a diagram illustrating a process of an image processing card 13 _1. FIG. 3 is a diagram for explaining processing of image processing cards 13 ₁ and 13 ₂ . FIG. 9 is a diagram illustrating a generation method for generating coefficient seed data from difference data. FIG. 9 is a block diagram illustrating a second configuration example of the image processing card 13. Is a diagram illustrating a process of an image processing card 13 _1. FIG. 3 is a diagram for explaining processing of image processing cards 13 ₁ and 13 ₂ . Is a diagram showing the real memory space of the image processing cards 13 _1. Is a diagram showing the real memory space of the image processing cards 13 _1. Is a diagram showing the real memory space of the image processing cards 13 _2. FIG. 4 is a diagram illustrating a state in which tap coefficients are stored in a virtual memory space. 5 is a flowchart illustrating a process of the image processing card. FIG. 4 is a diagram illustrating a method of generating coefficient seed data stored in an image processing card 13. FIG. 4 is a diagram illustrating a method of generating coefficient seed data stored in an image processing card 13. FIG. 4 is a diagram illustrating a method of generating coefficient seed data stored in an image processing card 13. FIG. 4 is a diagram illustrating a method of generating coefficient seed data stored in an image processing card 13. FIG. 4 is a block diagram illustrating a second configuration example of the image processing interface 40. FIG. 13 is a block diagram illustrating a third configuration example of the image processing card 13. 5 is a flowchart illustrating processing of an image processing interface. 5 is a flowchart illustrating a process of the image processing card. FIG. 7 is a diagram showing that the number of classes increases according to the number of image processing cards 13 mounted. It is a diagram illustrating a process of an image processing card 13 ₁ and the image processing interface 40. FIG. 3 is a diagram for explaining processing of the image processing cards 13 ₁ and 13 ₂ and the image processing interface 40. FIG. 3 is a diagram for explaining processing of the image processing cards 13 ₁ and 13 ₂ and the image processing interface 40. FIG. 7 is a diagram illustrating a correspondence relationship between tap coefficients of a first combined class code and tap coefficients of a second combined class code. 9 is a flowchart illustrating a method for generating generation information. 5 is a flowchart illustrating processing of a coefficient generation unit 136 of the image processing interface 40. FIG. 13 is a block diagram illustrating a third configuration example of the image processing interface 40. FIG. 14 is a block diagram illustrating a fourth configuration example of the image processing card 13. FIG. 2 is a block diagram illustrating a configuration example of a computer according to an embodiment of the present invention.

Explanation of reference numerals

1 body 2 remote control, 11 CRT, 12 ₁ to 12 ₆ slots 13 ₁ to 13 ₆ image processing card, 21 an antenna terminal, 22 an input terminal, 23 an output terminal, 31 a tuner, 32 A / D converter, 33 Y / C separation unit, 34 selector, 35 frame memory, 36 matrix conversion unit, 37 controller, 37A CPU, 37B EEPROM, 37C RAM, 38 communication interface, 39 IR interface, 40 image processing interface, 51 select button switch, 52 volume Button switch, 53 channel up / down button switch, 54 menu button switch, 55 exit button switch, 56 display button, 57 enter button switch, 58 numeric button (numeric keypad) switch, 59 TV / video switch button switch, 60 TV / DSS switch button switch, 61 jump button switch, 62 language button, 63 guide button switch, 64 favorite button switch, 65 cable button switch, 66 TV switch, 67 DSS button switch, 68 70 LED, 71 cable power button switch, 72 TV power button switch, 73 DSS power button switch, 74 muting button switch, 75 sleep button switch, 76 light emitting section, 81 interface controller, 82 memory interface, 83 card interface, 84 Connection detection unit, 85 line sequential conversion unit, 91, 92 tap extraction unit, 93 class classification unit, 94 coefficient memory, 95 prediction unit, 96 coefficient generation unit, 97 generation information storage unit, 98 card controller, 99 coefficient normalization unit, 100 memory space sharing control unit, 111 teacher data generation unit, 112 teacher data storage unit, 113 student data generation unit, 114 student data storage Section, 115, 116 tap extraction section, 117 class classification section, 118 addition section, 119 tap coefficient calculation section, 120 processing information generation section, 121 tap coefficient memory, 122 addition section, 123 coefficient type calculation section, 131, 132 Tap extraction unit, 133 class classification unit, 134 coefficient memory, 135 prediction unit, 136 coefficient generation unit, 141 memory space sharing control unit, 301 bus, 302 CPU, 303 ROM, 304 RAM, 305 hard disk, 306 output unit, 307 input Unit, 308 communication unit, 309 drive, 310 input / output interface, 311 removable recording medium

Claims (41)

In a storage device detachable from the data processing device,
Generation information storage for storing generation information for generating tap coefficients for each predetermined class for performing data conversion processing for converting first data into second data having higher quality than the first data. Means,
According to the control of the data processing device, from the generation information, a tap coefficient generation unit that generates the tap coefficient,
Prediction tap extraction means for extracting, from the first data supplied from the data processing device, a prediction tap used for predicting the data of interest of the second data,
Class tap extracting means for extracting a class tap used to perform a class classification for classifying the data of interest into any of a plurality of classes from the first data supplied from the data processing device;
Class classification means for classifying the data of interest based on the class tap;
A storage device, comprising: prediction means for predicting the data of interest from a tap coefficient of a class of the data of interest and the prediction tap, and supplying the data to the data processing device. The storage device according to claim 1, wherein the generation information storage unit stores coefficient seed data serving as a seed of the tap coefficient as the generation information. In the data processing device, other storage devices are also detachable,
The generation information storage unit stores, as the generation information, information capable of generating its own coefficient seed data using another coefficient seed data stored in the other storage device. A storage device according to claim 1. The storage device according to claim 3, wherein the generation information storage unit stores, as the generation information, a difference between the own coefficient seed data and another coefficient seed data in the another storage device. . In the data processing device, other storage devices are also detachable,
The tap coefficient generation unit generates the tap coefficient from the generation information stored in the generation information storage unit and other generation information stored in the other storage device supplied from the data processing device. The storage device according to claim 1, wherein: Tap coefficient storage means for storing the tap coefficient,
2. The storage device according to claim 1, further comprising: a tap coefficient acquisition unit configured to acquire a tap coefficient of a class of the data of interest from the tap coefficients stored in the tap coefficient storage unit. 3. In the data processing device, other storage devices are also detachable,
The tap coefficient generation unit generates the tap coefficient having a size exceeding the storage capacity of the tap coefficient storage unit,
The tap coefficient generated by the tap coefficient generation unit is stored in the tap coefficient storage unit and the other storage device,
The storage device according to claim 6, wherein the tap coefficient acquisition unit acquires a tap coefficient of a class of the data of interest from the tap coefficient storage unit or the other storage device. The storage device according to claim 1, wherein the tap coefficient generation unit generates the tap coefficient by performing an operation according to a predetermined operation expression defined by the generation information. In the data processing device, other storage devices are also detachable,
The said tap coefficient generation means produces | generates the said tap coefficient by performing operation according to the said predetermined | prescribed arithmetic expression which has more terms than the arithmetic expression used by the said another memory | storage device. A storage device according to claim 1. In the data processing device, other storage devices are also detachable,
The storage device according to claim 1, wherein the tap coefficient generation unit generates a tap coefficient having a larger number of classes than the number of classes of the tap coefficient generated in the another storage device. In the data processing device, other storage devices are also detachable,
The generation information storage unit stores generation information for generating a tap coefficient for performing the data conversion process for performing a quality improvement different from a tap coefficient generated by the other storage device. 2. The storage device according to 1. A storage device that is detachable from a data processing device, and is a tap coefficient for each predetermined class for performing a data conversion process of converting first data into second data having higher quality than the first data. In a data processing method of a storage device having generation information storage means for storing generation information for generating
According to the control of the data processing device, a tap coefficient generation step of generating the tap coefficient from the generation information,
A prediction tap extraction step of extracting, from the first data supplied from the data processing device, a prediction tap used to predict a target data of interest of the second data;
A class tap extraction step of extracting, from the first data supplied from the data processing device, a class tap used to perform a class classification for classifying the data of interest into any of a plurality of classes;
A class classification step of classifying the data of interest based on the class tap;
A data processing method, comprising: predicting the data of interest from a tap coefficient of a class of the data of interest and the prediction tap, and supplying the data to the data processing device. A storage device that is detachable from a data processing device, and is a tap coefficient for each predetermined class for performing a data conversion process of converting first data into second data having higher quality than the first data. In a program for causing a computer to perform data processing in a storage device having generation information storage means for storing generation information for generating
According to the control of the data processing device, a tap coefficient generation step of generating the tap coefficient from the generation information,
A prediction tap extraction step of extracting, from the first data supplied from the data processing device, a prediction tap used to predict a target data of interest of the second data;
A class tap extraction step of extracting, from the first data supplied from the data processing device, a class tap used to perform a class classification for classifying the data of interest into any of a plurality of classes;
A class classification step of classifying the data of interest based on the class tap;
A program for predicting the data of interest from a tap coefficient of a class of the data of interest and the prediction tap, and supplying the predicted data to the data processing device. A storage device that is detachable from a data processing device, and is a tap coefficient for each predetermined class for performing a data conversion process of converting first data into second data having higher quality than the first data. The data processing in the storage device having the generation information storage means for storing the generation information for generating the
According to the control of the data processing device, a tap coefficient generation step of generating the tap coefficient from the generation information,
A prediction tap extraction step of extracting, from the first data supplied from the data processing device, a prediction tap used to predict a target data of interest of the second data;
A class tap extraction step of extracting, from the first data supplied from the data processing device, a class tap used to perform a class classification for classifying the data of interest into any of a plurality of classes;
A class classification step of classifying the data of interest based on the class tap;
A recording medium recording a program comprising: a prediction step of predicting the data of interest from a tap coefficient of a class of the data of interest and the prediction tap and supplying the data to the data processing device. First to third information storing generation information for generating tap coefficients for each predetermined class for performing data conversion processing for converting the first data into second data having higher quality than the first data. In a data processing device to which the Nth storage device is detachable,
Attaching / detaching means to which the first to Nth storage means are attached;
Tap coefficient generation control means for controlling generation of the tap coefficient from the generation information in the first to N'th (≤N) storage devices attached to the attachment / detachment means;
Input / output route setting means for setting an input / output route of data for each of the first to N'th storage devices;
Data supply for controlling data supply from one of the first to N ′ storage units to another storage unit according to the input / output route set by the input / output route setting unit. A data processing device comprising: a control unit. The one storage device stores coefficient seed data serving as a seed of the tap coefficient as the generation information,
The other one storage device stores information capable of generating its own coefficient seed data using the coefficient seed data stored in the one storage device, as the generation information,
The tap coefficient generation control unit uses the coefficient seed data stored in the one storage device and the generation information stored in the another storage device to generate a value of the other storage device itself. The data processing apparatus according to claim 15, wherein the other one storage device is controlled so as to generate coefficient seed data and generate a tap coefficient from the coefficient seed data. The other one storage device stores a difference between its own coefficient seed data and the coefficient seed data in the one storage device as the generation information,
The tap coefficient generation control means generates its own coefficient seed data by adding the coefficient seed data in the one storage device and the generation information stored in the other storage device, and generates the coefficient seed data. The data processing apparatus according to claim 16, wherein the other one storage device is controlled so as to generate a tap coefficient from the seed data. The tap coefficient generation control means is configured to generate the tap coefficient from the generation information stored in the one storage device and the generation information stored in the another storage device. The data processing device according to claim 15, wherein the data processing device controls two storage devices. First to third information storing generation information for generating tap coefficients for each predetermined class for performing data conversion processing for converting the first data into second data having higher quality than the first data. In the data processing method of a data processing device, wherein the N-th storage device is a detachable data processing device, and the first to N-th storage devices are attached and detached.
A tap coefficient generation control step of controlling generation of the tap coefficient from the generation information in the first to N ′ (≦ N) storage devices attached to the attaching / detaching means;
An input / output route setting step of setting an input / output route of data for each of the first to N'th storage devices;
Data supply for controlling data supply from one storage device of the first to N ′ storage units to another storage device according to the input / output route set in the input / output route setting step. A data processing method, comprising: a control step. First to third information storing generation information for generating tap coefficients for each predetermined class for performing data conversion processing for converting the first data into second data having higher quality than the first data. A program for causing a computer to perform data processing in a data processing device in which the N-th storage device is a detachable data processing device, the data processing device having a detachable device to which the first to N-th storage devices are attached.
A tap coefficient generation control step of controlling generation of the tap coefficient from the generation information in the first to N ′ (≦ N) storage devices attached to the attaching / detaching means;
An input / output route setting step of setting an input / output route of data for each of the first to N'th storage devices;
Data supply for controlling data supply from one storage device of the first to N ′ storage units to another storage device according to the input / output route set in the input / output route setting step. A program comprising: a control step. First to third information storing generation information for generating tap coefficients for each predetermined class for performing data conversion processing for converting the first data into second data having higher quality than the first data. A program for causing a computer to perform data processing in a data processing device in which the N-th storage device is a detachable data processing device and has a detachable device to which the first to N-th storage devices are attached is recorded. Recording media,
A tap coefficient generation control step of controlling generation of the tap coefficient from the generation information in the first to N ′ (≦ N) storage devices attached to the attaching / detaching means;
An input / output route setting step of setting an input / output route of data for each of the first to N'th storage devices;
Data supply for controlling data supply from one storage device of the first to N ′ storage units to another storage device according to the input / output route set in the input / output route setting step. A recording medium characterized by recording a program comprising: a control step; First to third information storing generation information for generating tap coefficients for each predetermined class for performing data conversion processing for converting the first data into second data having higher quality than the first data. An N-th storage device;
A data processing system comprising the first to Nth storage devices and a detachable data processing device;
Each of the first to Nth storage devices includes:
Generation information storage means for storing generation information for generating the tap coefficient,
According to the control of the data processing device, from the generation information, a tap coefficient generation unit that generates the tap coefficient,
Prediction tap extraction means for extracting, from the first data supplied from the data processing device, a prediction tap used for predicting the data of interest of the second data,
Class tap extracting means for extracting a class tap used to perform a class classification for classifying the data of interest into any of a plurality of classes from the first data supplied from the data processing device;
Class classification means for classifying the data of interest based on the class tap;
Prediction means for predicting the data of interest from a tap coefficient of a class for the data of interest and the prediction tap, and supplying the data to the data processing device;
The data processing device includes:
Attaching / detaching means to which the first to Nth storage means are attached;
Tap coefficient generation control means for controlling generation of the tap coefficient from the generation information in the first to N'th (≤N) storage devices attached to the attachment / detachment means;
Input / output route setting means for setting an input / output route of data for each of the first to N'th storage devices;
Data supply for controlling data supply from one of the first to N ′ storage units to another storage unit according to the input / output route set by the input / output route setting unit. A data processing system comprising: control means; First to third information storing generation information for generating tap coefficients for each predetermined class for performing data conversion processing for converting the first data into second data having higher quality than the first data. In a data processing device to which the Nth storage device is detachable,
Attaching / detaching means to which the first to Nth storage means are attached;
Tap coefficient generation means for generating the tap coefficient from the generation information in the first to N ′ (≦ N) storage devices attached to the attachment / detachment means;
Predictive tap extracting means for extracting the first data as a predictive tap used to predict target data of interest in the second data;
Class tap extracting means for extracting the first data as class taps used for performing class classification for classifying the data of interest into any of a plurality of classes;
Class classification means for classifying the data of interest based on the class tap;
A data processing apparatus comprising: a prediction unit configured to predict the data of interest from a tap coefficient of a class of the data of interest and the prediction tap. The second data obtained by using the tap coefficients generated from the generation information in the first to N′th storage devices is obtained from the generation information in the first to N′−1 storage devices. 24. The data processing device according to claim 23, wherein the data has higher quality than the second data obtained using the generated tap coefficient. The tap coefficients generated from the generation information in the first to N'th storage devices are different from the tap coefficients generated from the generation information in the first to N'-1 storage devices. The data processing device according to claim 23, wherein: The tap coefficient generation means generates the tap coefficient by performing an operation according to a predetermined operation expression defined by the generation information in the first to N'th storage devices. 24. The data processing device according to 23. The arithmetic expressions defined by the generation information in the first to N'th storage devices have more terms than the arithmetic expressions defined by the generation information in the first to N'-1 storage devices. The data processing device according to claim 26, wherein: In a case where each of the first to Nth storage devices includes a tap coefficient storage unit that stores the tap coefficient generated by the tap coefficient generation unit,
The tap coefficients generated from the generation information in the first to N'th storage devices are larger than the sum of the storage capacities of the tap coefficient storage units of the first to N'-1 storage devices. 24. The data processing device according to claim 23, wherein the data processing device has a large size and is equal to or smaller than a total sum of storage capacities of the tap coefficient storage units included in the first to N'th storage devices. In a case where each of the first to Nth storage devices includes a tap coefficient storage unit that stores the tap coefficient generated by the tap coefficient generation unit,
The tap coefficients generated from the generation information in the first to N'th storage devices are stored over the tap coefficient storage means included in each of the first to N'th storage devices. The data processing device according to claim 28, wherein The tap coefficients generated from the generation information in the first to N'th storage devices are more class than the tap coefficients generated from the generation information in the first to N'-1 storage devices. The data processing device according to claim 23, wherein the number is large. The data according to claim 23, wherein the tap coefficient generation unit generates a set of tap coefficients from the generation information in one of the first to N'th storage devices. Processing equipment. When only the first storage device is attached to the attaching / detaching means, the one set of tap coefficients generated from the generation information in the first storage device performs N kinds of quality improvement. Yes,
When the first to N′th storage devices of the first to Nth storage devices are mounted on the attaching / detaching means, the first to N′th storage devices are respectively generated from the generation information in the first to Nth storage devices. The data processing device according to claim 31, wherein the N sets of tap coefficients to be performed correspond to the N types of quality improvements, respectively. Performing N ′ times the data conversion process on the first data by using N ′ types of tap coefficients generated from the generation information in the first to N′th storage devices. 32. The data processing device according to claim 31, wherein The tap coefficient generation unit generates the tap coefficient from the coefficient seed data, using the generation information in the first to N′th storage devices as coefficient seed data serving as a seed of the tap coefficient. The data processing device according to claim 23, wherein The N′th storage device generates the N′th coefficient seed data using the N′−1th coefficient seed data obtained from the generation information in the first to N′−1th storage devices. Possible information is stored as the generation information,
The tap coefficient generating means generates the N'th coefficient seed data from the generation information in the first to N'th storage devices, and further generates the tap coefficient from the N'th coefficient seed data. 35. The data processing device according to claim 34, wherein: The difference between the N′th coefficient seed data and the N′−1th coefficient seed data is stored in the N′th storage device as the generation information,
The tap coefficient generation unit may calculate the N′−1th coefficient seed data obtained from the generation information in the first to N′−1th storage devices and the generation information in the N′th storage device. The data processing apparatus according to claim 35, wherein the N'th coefficient seed data is generated by adding. Classification requesting means for requesting the first to N'th storage devices to classify the data of interest,
The class classifying unit synthesizes information indicating the class of the data of interest supplied from each of the first to N'th storage devices in response to the request for the class classification, thereby obtaining the final data of the data of interest. The data processing apparatus according to claim 23, wherein a general class is obtained. First to third information storing generation information for generating tap coefficients for each predetermined class for performing data conversion processing for converting the first data into second data having higher quality than the first data. In a data processing method of a data processing device in which an N-th storage device is detachable,
A tap coefficient generation step of generating the tap coefficient from the generation information in the first to N ′ (≦ N) storage devices mounted on the data processing device;
A prediction tap extracting step of extracting the first data as a prediction tap used for predicting the noted data of interest in the second data;
A class tap extracting step of extracting the first data as a class tap used for performing a class classification for classifying the target data into any of a plurality of classes;
A class classification step of classifying the data of interest based on the class tap;
A data processing method, comprising: a prediction step of predicting the data of interest from a tap coefficient of a class of the data of interest and the prediction tap. First to third information storing generation information for generating tap coefficients for each predetermined class for performing data conversion processing for converting the first data into second data having higher quality than the first data. In a program for causing a computer to perform data processing in a data processing device to which the Nth storage device is detachable,
A tap coefficient generation step of generating the tap coefficient from the generation information in the first to N ′ (≦ N) storage devices mounted on the data processing device;
A prediction tap extracting step of extracting the first data as a prediction tap used for predicting the noted data of interest in the second data;
A class tap extracting step of extracting the first data as a class tap used for performing a class classification for classifying the target data into any of a plurality of classes;
A class classification step of classifying the data of interest based on the class tap;
A program for predicting the data of interest from a tap coefficient of a class of the data of interest and the prediction tap. First to third information storing generation information for generating tap coefficients for each predetermined class for performing data conversion processing for converting the first data into second data having higher quality than the first data. In a recording medium storing a program for causing a computer to perform data processing in a data processing device to which the Nth storage device is detachable,
A tap coefficient generation step of generating the tap coefficient from the generation information in the first to N ′ (≦ N) storage devices mounted on the data processing device;
A prediction tap extracting step of extracting the first data as a prediction tap used for predicting the noted data of interest in the second data;
A class tap extracting step of extracting the first data as a class tap used for performing a class classification for classifying the target data into any of a plurality of classes;
A class classification step of classifying the data of interest based on the class tap;
A recording medium storing a program comprising: a prediction step of predicting the data of interest from a tap coefficient of a class of the data of interest and the prediction tap. First to third information storing generation information for generating tap coefficients for each predetermined class for performing data conversion processing for converting the first data into second data having higher quality than the first data. An N-th storage device;
A data processing system comprising the first to Nth storage devices and a detachable data processing device;
Each of the first to Nth storage devices includes:
Generation information storage means for storing the generation information, the generation information and the generation information stored in another storage device, the tap coefficient is obtained from the generated information,
A generation information supply unit that supplies the generation information to the data processing device;
The data processing device includes:
Attaching / detaching means to which the first to Nth storage means are attached;
Tap coefficient generation means for generating the tap coefficient from the generation information in the first to N ′ (≦ N) storage devices attached to the attachment / detachment means;
Predictive tap extracting means for extracting the first data as a predictive tap used to predict target data of interest in the second data;
Class tap extracting means for extracting the first data as class taps used for performing class classification for classifying the data of interest into any of a plurality of classes;
Class classification means for classifying the data of interest based on the class tap;
A data processing system comprising: prediction means for predicting the data of interest from a tap coefficient of a class for the data of interest and the prediction tap.

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