JPH1084549A - Image coder, image coding method, image decoder, image decoding method, transmission method and recording medium - Google Patents

Abstract

PROBLEM TO BE SOLVED: To efficiently obtain a decoded image nearly the same as an original image with a small data processing quantity. SOLUTION: A level limit circuit 31 limits the level of compression data obtained by extracting one picture element from 9 picture elements of an original image a thinning circuit 30 by extracting two bits from the MSB. A local decode section predicts the original image based on correction data and its predicted value is outputted. Then an error of a prediction value is calculated with respect to an original image in an error calculation section. A correction circuit 32 corrects the compression data so as to reduce the prediction error.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an image encoding apparatus and an image encoding method, an image decoding apparatus and an image decoding method, a transmission method, and a recording medium. The present invention relates to an image encoding device and an image encoding method, an image decoding device and an image decoding method, a transmission method, and a recording medium that can obtain a higher-quality predicted image.

[0002]

2. Description of the Related Art Conventionally, image compression methods have been described.
Various methods have been proposed, one of which is to compress an image by thinning out its pixels.

[0003]

However, if pixel data simply compressed by thinning out an image is directly encoded, the amount of data processing becomes enormous, and a high-quality predicted image (decoded image) is efficiently obtained. There was a problem that could not be obtained.

[0004] The present invention has been made in view of such circumstances, and it is an object of the present invention to be able to efficiently obtain a higher quality predicted image with a small amount of data.

[0005]

According to a first aspect of the present invention, there is provided an image encoding apparatus for limiting a level of compressed data obtained by compressing an original image, and correcting the level-limited compressed data. A correction unit that outputs correction data, a prediction unit that predicts an original image based on the correction data, and outputs a prediction value thereof; a calculation unit that calculates a prediction error of a prediction value with respect to the original image; Based on the error,
A determination unit that determines the appropriateness of the correction data output by the correction unit; and an output unit that outputs the correction data as an encoding result of the original image in accordance with the determination result by the determination unit. .

According to an image encoding method of the present invention, the level of compressed data obtained by compressing an original image is limited, and the level-compressed compressed data is corrected.
Outputting the correction data, predicting the original image based on the correction data, outputting the predicted value, calculating the prediction error of the predicted value with respect to the original image, and determining the correctness of the correction data based on the prediction error. Is repeated until the correction data becomes appropriate, and the corrected data that has become appropriate is output as a result of encoding the original image.

According to a twelfth aspect of the present invention, in the image decoding apparatus, the encoded data limits the level of the compressed data obtained by compressing the original image, and corrects the limited-level compressed data. Output the data, predict the original image based on the correction data, output the predicted value, calculate the prediction error of the predicted value with respect to the original image, and determine the appropriateness of the correction data based on the prediction error. It is characterized in that the determination includes proper correction data obtained by repeating the determination until the correction data becomes proper.

[0008] In the image decoding method according to the present invention, the encoded data may be obtained by limiting the level of the compressed data obtained by compressing the original image, and correcting the compressed data having the limited level. Output the data, predict the original image based on the correction data, output the predicted value, calculate the prediction error of the predicted value with respect to the original image, and determine the appropriateness of the correction data based on the prediction error. It is characterized in that the determination includes proper correction data obtained by repeating the determination until the correction data becomes proper.

According to a nineteenth aspect of the present invention, in the transmission method, the coded data restricts the level of the compressed data obtained by compressing the original image, corrects the restricted compressed data, and converts the corrected data. Output, predict an original image based on the correction data, output a prediction value thereof, calculate a prediction error of a prediction value with respect to the original image, and determine appropriateness of the correction data based on the prediction error. This is characterized by including corrected correction data obtained by repeating this until the correction data becomes proper.

According to a twentieth aspect of the present invention, in the recording medium, the encoded data compresses the original image by reducing the number of pixels thereof, and restricts the level of compressed data obtained by compressing the original image. The compressed data with the limited level is corrected, the corrected data is output, the original image is predicted based on the corrected data, the predicted value is output, and the prediction error of the predicted value with respect to the original image is calculated. And determining that the correctness of the correction data based on the prediction error includes the corrected correction data obtained by repeating until the correction data becomes proper.

According to a twenty-first aspect of the present invention, an image coding apparatus stores a classifying means for classifying pixels constituting an image into predetermined classes according to their properties, and stores a predetermined mapping coefficient for each class. By performing a predetermined operation using a mapping coefficient storage unit and a target pixel of interest in the image and a mapping coefficient corresponding to the class of the target pixel, correction data obtained by correcting the target pixel is obtained. It is characterized by comprising a calculating means for calculating, and a limiting means for limiting the level of the correction data to convert the image into encoded data.

According to an image coding method of the present invention, pixels constituting an image are classified into predetermined classes according to their properties, and a predetermined mapping coefficient is stored for each class. From the means, a mapping coefficient corresponding to the class of the pixel of interest in the image is read out, and a predetermined operation is performed using the mapping coefficient and the pixel of interest, thereby correcting the pixel of interest. Calculate the data, limit the level of correction data,
It is characterized in that the image is encoded data.

According to a thirty-fifth aspect of the present invention, in the image decoding apparatus, the encoded data classifies pixels constituting an image into one of first classes according to their properties, and In the image, a mapping coefficient corresponding to a first class of a target pixel of interest in an image is read from a mapping coefficient storage unit that stores a predetermined mapping coefficient, and the mapping coefficient and the target pixel are read. By performing a predetermined calculation using the data, correction data for correcting the target pixel is calculated, and the correction data is obtained by limiting the level of the correction data.

According to a thirty-seventh aspect of the present invention, in the image decoding method, the coded data classifies the pixels constituting the image into any one of predetermined classes according to their properties, and for each class, a predetermined class is used. A mapping coefficient corresponding to a class of a target pixel of interest in an image is read from a mapping coefficient storage unit that stores the mapping coefficient, and a predetermined operation is performed using the mapping coefficient and the target pixel. Thereby, the correction data obtained by correcting the target pixel is calculated, and the correction data is obtained by limiting the level of the correction data.

[0015] In the transmission method according to claim 38, the coded data classifies the pixels constituting the image into predetermined classes according to their properties, and stores a predetermined mapping coefficient for each class. From the mapping coefficient storage means, a mapping coefficient corresponding to the class of the pixel of interest in the image is read out, and a predetermined operation is performed using the mapping coefficient and the pixel of interest. It is characterized by being obtained by calculating corrected correction data and limiting the level of correction data.

In a recording medium according to a thirty-ninth aspect, the encoded data classifies pixels constituting an image into predetermined classes according to their properties, and stores a predetermined mapping coefficient for each class. From the mapping coefficient storage means, a mapping coefficient corresponding to the class of the pixel of interest in the image is read out, and a predetermined operation is performed using the mapping coefficient and the pixel of interest. It is characterized by being obtained by calculating corrected correction data and limiting the level of correction data.

In the image coding apparatus according to the present invention, the limiting means limits the level of the compressed data obtained by compressing the original image, and the correcting means corrects the compressed data having the limited level. Then, the correction data is output. The prediction means predicts the original image based on the correction data and outputs a predicted value, and the calculation means calculates a prediction error of a predicted value with respect to the original image. The determining unit determines the appropriateness of the correction data output by the correcting unit based on the prediction error, and the output unit outputs the corrected data as an encoding result of the original image according to the determination result by the determining unit. It has been made to be.

In the image encoding method according to the eleventh aspect, the level of the compressed data obtained by compressing the original image is limited, the level-limited compressed data is corrected, and the corrected data is output. , Based on the correction data,
Predict the original image, output the predicted value, calculate the prediction error of the predicted value for the original image, based on the prediction error,
The determination of the correctness of the correction data is repeated until the correction data becomes appropriate, and the corrected correction data is output as the encoding result of the original image.

In the image decoding device according to the twelfth aspect, the image decoding method according to the eighteenth aspect, the transmission method according to the nineteenth aspect, and the recording medium according to the twentieth aspect, the encoded data is Compressing the original image by reducing its number of pixels, limiting the level of compressed data obtained by compressing the original image, correcting the level-limited compressed data, and outputting corrected data. Predicting the original image based on the correction data, outputting the predicted value thereof, calculating the prediction error of the predicted value with respect to the original image, and determining the appropriateness of the correction data based on the prediction error. , And correction data obtained by repeating until the correction data becomes proper.

In the image coding apparatus according to the present invention, the classifying means classifies the pixels constituting the image into a predetermined class according to their properties, and the mapping coefficient storage means sets a predetermined class for each class. Are stored. The calculating means performs a predetermined calculation using the target pixel of interest in the image and a mapping coefficient corresponding to the class of the target pixel, thereby calculating correction data for correcting the target pixel. The restricting means restricts the level of the correction data so that the image is encoded data.

In the image coding method according to the present invention, pixels constituting an image are classified into predetermined classes according to their properties, and a predetermined mapping coefficient is stored for each class. From the storage unit, the mapping coefficient corresponding to the class of the pixel of interest in the image is read out, and the pixel of interest is corrected by performing a predetermined operation using the mapping coefficient and the pixel of interest. Correction data is calculated, the level of the correction data is limited, and encoded data is obtained by encoding an image.

[0022] In the image decoding apparatus according to claim 30, the image decoding method according to claim 37, the transmission method according to claim 38, and the recording medium according to claim 39, the encoded data is The pixels constituting the image are classified into predetermined classes according to their properties, and for each class, a target pixel of interest in the image from a mapping coefficient storage unit storing a predetermined mapping coefficient. By reading out the mapping coefficient corresponding to the class of, and performing a predetermined operation using the mapping coefficient and the pixel of interest, the correction data corrected for the pixel of interest is calculated, and the level of the correction data is limited. It has been obtained.

[0023]

DESCRIPTION OF THE PREFERRED EMBODIMENTS Embodiments of the present invention will be described below, but before that, the correspondence between each means of the invention described in the claims and the following embodiments will be clarified. For this reason, the features of the present invention are described as follows by adding the corresponding embodiment (however, an example) in parentheses after each means.

That is, an image coding apparatus according to claim 1 is an image coding apparatus for coding an image, wherein a compression means for compressing an original image by reducing the number of pixels (for example, FIG. 3) and limiting means for limiting the level of compressed data obtained by compressing the original image (eg, the compression unit 21 shown in FIG. 3).
Correction means for correcting the compressed data having a limited level and outputting the corrected data (for example, the compression unit 21 shown in FIG. 3).
And a prediction unit (for example, the local decoding unit 22 shown in FIG. 3) for predicting the original image based on the correction data and outputting the predicted value, and calculating a prediction error of the predicted value with respect to the original image. (For example, the error calculating unit 23 shown in FIG. 3) and a judging unit (for example, the judging unit 24 shown in FIG. 3 etc.) for judging the appropriateness of the correction data output by the correcting unit based on the prediction error. ) And an output unit (for example, the determination unit 24 shown in FIG. 3) that outputs correction data as an encoding result of the original image in accordance with the determination result by the determination unit.

According to a second aspect of the present invention, in the image coding apparatus, the predicting means includes a classifying means (for example, a class classifying circuit 45 shown in FIG. 8) for classifying the correction data into a predetermined class according to the property thereof. , A prediction value calculating means (for example, an adaptive processing circuit 46 shown in FIG. 8) for obtaining a prediction value corresponding to each class.

According to a third aspect of the present invention, in the image coding apparatus, the prediction means calculates a prediction coefficient for calculating a prediction value by linear combination with the correction data.
An adaptive processing circuit 46 shown in FIG. 8) and a predicted value calculating means (for example, the adaptive processing circuit 46 shown in FIG. 8) for obtaining a predicted value from a prediction coefficient and correction data are characterized.

According to a fourth aspect of the present invention, in the image coding apparatus, the predicting means includes a classifying means (for example, a class classifying circuit 45 shown in FIG. 8) for classifying the correction data into a predetermined class according to the nature of the correction data. The prediction coefficient calculation means (for example, the adaptive processing circuit 46 shown in FIG. 8) for obtaining a prediction coefficient for calculating a prediction value by linear combination with the correction data and the correction data class is obtained. It is characterized by having a predicted value calculating means (for example, an adaptive processing circuit 46 shown in FIG. 8) for obtaining a predicted value from a prediction coefficient and its correction data.

According to a sixth aspect of the present invention, in the image coding apparatus, the prediction means stores a prediction coefficient for calculating a prediction value by linear combination with the correction data for each predetermined class (for example, , A prediction coefficient ROM 81 shown in FIG. 17, and a classifying means (for example, FIG. 17) for classifying correction data into one of predetermined classes according to the nature of the correction data.
And a prediction value calculating means (for example, a prediction circuit 82 shown in FIG. 17) for obtaining a prediction value from a prediction coefficient for a class of correction data and the correction data. It is characterized by.

According to a ninth aspect of the present invention, in the image coding apparatus, the correction means has a storage means for storing a correction value for correcting the compressed data (for example, a correction value ROM 33 shown in FIG. 5). The compressed data is corrected using the correction value.

An image decoding apparatus according to a twelfth aspect is an image decoding apparatus that decodes encoded data obtained by encoding an image, and includes a receiving unit that receives the encoded data (for example, as shown in FIG. 15). A receiver / reproducing device 71) and decoding means for decoding encoded data (for example, a classifying blocking circuit 73 and a classifying circuit 7 shown in FIG. 15).
5, a prediction circuit 76, a prediction value calculation blocking circuit 77, etc.), and the encoded data is obtained by compressing the original image by reducing the number of pixels and compressing the original image. Limiting the level of the compressed data, correcting the level-limited compressed data, outputting correction data, predicting the original image based on the correction data, outputting the predicted value, Calculating the prediction error of the prediction value, and determining the correctness of the correction data based on the prediction error, including the corrected correction data obtained by repeating until the correction data becomes appropriate. Features.

According to a thirteenth aspect of the present invention, in the image decoding apparatus, the decoding means classifies the correction data into a predetermined class according to the nature of the correction data (for example, a class classification circuit 75 shown in FIG. 15). And prediction value calculating means for obtaining a prediction value corresponding to the class (for example, the prediction circuit 7 shown in FIG. 15).
6 etc.).

According to a fourteenth aspect of the present invention, in the image decoding apparatus, the encoded data also includes a prediction coefficient for calculating a prediction value by a linear combination with the correction data.
It is characterized by having a predicted value calculation means (for example, a prediction circuit 76 shown in FIG. 15) for obtaining a predicted value from a prediction coefficient and correction data.

According to a fifteenth aspect of the present invention, in the image decoding apparatus, the encoded data also includes a prediction coefficient for each predetermined class for calculating a prediction value by a linear combination with the correction data. Is based on classification means (for example, a class classification circuit 75 shown in FIG. 15) for classifying the correction data into a predetermined class according to its properties, prediction coefficients for the class of the correction data, and the correction data. And a prediction value calculating means (for example, a prediction circuit 76 shown in FIG. 15) for obtaining a prediction value.

In the image decoding apparatus according to the present invention, the decoding means stores a prediction coefficient for calculating a prediction value by linear combination with correction data for each predetermined class. For example, the prediction coefficient ROM 7 shown in FIG.
8), a classifying means (for example, a class classification circuit 75 shown in FIG. 26) for classifying the correction data into one of predetermined classes according to its properties, and a prediction coefficient for the class of the correction data. A prediction value calculation means (for example, a prediction circuit 76 shown in FIG. 26) which reads out from the means and obtains a prediction value from the prediction coefficient and the correction data.

An image coding apparatus according to a twenty-first aspect is an image coding apparatus for coding an image, wherein a classifying means for classifying pixels constituting the image into a predetermined class according to their properties (for example, , A class classification circuit 113 shown in FIG. 27), a mapping coefficient storage means (for example, a mapping coefficient memory 114 shown in FIG. 27) for storing predetermined mapping coefficients for each class, A calculation unit that performs a predetermined calculation using a target pixel of interest and a mapping coefficient corresponding to the class of the target pixel to calculate correction data that corrects the target pixel (for example, as illustrated in FIG. 27). Arithmetic circuit 116, etc.)
There is provided a limiting means (for example, a level limiting circuit 117 shown in FIG. 27) for limiting the level of the correction data to convert the image into encoded data.

An image encoding method according to claim 29 is an image encoding method for encoding an image, in which pixels constituting the image are classified into predetermined classes according to their properties. A mapping coefficient corresponding to the class of the pixel of interest in the image is read out from a mapping coefficient storage means (for example, the mapping coefficient memory 114 shown in FIG. 27) storing a predetermined mapping coefficient. By performing a predetermined operation using the mapping coefficient and the pixel of interest, correction data obtained by correcting the pixel of interest is calculated, and the level of the correction data is limited to obtain encoded data obtained by encoding the image. It is characterized by the following.

An image decoding apparatus according to claim 30 is an image decoding apparatus for decoding coded data obtained by coding an image, and a receiving means for receiving the coded data (for example, as shown in FIG. 37). A receiver / reproducing device 191) and a decoding unit (eg, a decoding unit 192 shown in FIG. 37) for decoding the encoded data. , Classified into any of the first classes, and for each first class,
A mapping coefficient storage means storing a predetermined mapping coefficient (for example, mapping coefficient memory 1 shown in FIG. 27).
14), a mapping coefficient corresponding to the first class of the target pixel of interest in the image is read out, and a predetermined operation is performed using the mapping coefficient and the target pixel to obtain the target pixel. It is characterized in that the correction data is obtained by calculating correction data obtained by correcting pixels and limiting the level of the correction data.

According to a thirty-second aspect of the present invention, in the image decoding apparatus, the decoding means stores, for each of the second classes, a prediction coefficient for calculating a prediction value by a linear combination with the encoded data. A coefficient storage means (for example, the prediction coefficient ROM 197 shown in FIG. 37) and a classification means (for example, a class classification shown in FIG. 37) for classifying the coded data into one of the second classes according to its properties. Circuit 196)
And a prediction coefficient for a second class of encoded data from a prediction coefficient storage means, and a prediction value calculation means for obtaining a prediction value from the prediction coefficient and the encoded data (for example, a prediction circuit shown in FIG. 37). 198).

An image decoding method according to a thirty-seventh aspect is an image decoding method for decoding coded data obtained by coding an image, wherein the coded data includes pixels constituting the image.
The image data is classified into one of the predetermined classes according to its properties, and the image data is stored in a mapping coefficient storage unit (for example, the mapping coefficient memory 114 shown in FIG. 27) storing predetermined mapping coefficients for each class. Of the target pixel of interest, a mapping coefficient corresponding to the class of the target pixel of interest is read, and a predetermined operation is performed using the mapping coefficient and the target pixel, thereby calculating correction data for correcting the target pixel. , Which are obtained by limiting the level of the correction data.

A transmission method according to a thirty-eighth aspect of the present invention is a transmission method for transmitting coded data obtained by coding an image, wherein the coded data converts pixels constituting the image into a predetermined class according to the nature of the pixels. From the mapping coefficient storage means (for example, the mapping coefficient memory 114 shown in FIG. 27) that stores a predetermined mapping coefficient for each class. A mapping coefficient corresponding to the class of the pixel of interest is read out, and a predetermined operation is performed using the mapping coefficient and the pixel of interest, thereby calculating correction data for correcting the pixel of interest and limiting the level of the correction data. It is characterized by having been obtained by this.

A recording medium according to a thirty-ninth aspect of the present invention is a recording medium on which encoded data obtained by encoding an image is recorded, wherein the encoded data specifies pixels constituting the image in accordance with a property thereof. From the mapping coefficient storage means (for example, the mapping coefficient memory 114 shown in FIG. 27) storing predetermined mapping coefficients for each class. The mapping coefficient corresponding to the class of the pixel of interest is read out, and a predetermined operation is performed using the mapping coefficient and the pixel of interest, thereby calculating correction data for correcting the pixel of interest, and setting the level of the correction data. It is characterized by being obtained by restricting.

Of course, this description does not mean that each means is limited to those described above.

FIG. 1 shows the configuration of an embodiment of an image processing apparatus to which the present invention is applied. The transmitting device 1 is supplied with digitized image data. The transmission device 1 compresses and encodes the input image data by thinning out (decreasing the number of pixels), and converts the resulting encoded data into, for example, an optical disk, a magneto-optical disk, a magnetic tape, a phase change It is recorded on a recording medium 2 such as a disk or the like, or transmitted via a transmission line 3 such as a terrestrial wave, a satellite line, a telephone line, a CATV network, the Internet, or the like.

The receiving device 4 reproduces the encoded data recorded on the recording medium 2 or receives the encoded data transmitted via the transmission path 3, and expands and decodes the encoded data. Is done. Then, the decoded image obtained as a result is supplied to a display (not shown) and displayed.

The above-described image processing apparatus is, for example, an optical disk device, a magneto-optical disk device, a magnetic tape device, or another device for recording / reproducing an image, or, for example, a videophone device, a television, or the like. The present invention is applied to a broadcast system, a CATV system, and other devices that transmit images. Further, as described later, since the data amount of the encoded data output from the transmission device 1 is small, the image processing device in FIG. 1 has a low transmission rate, such as a mobile phone or other portable terminal that is convenient for movement. Is also applicable.

FIG. 2 shows a configuration example of the transmission device 1.

An I / F (InterFace) 11 receives image data supplied from the outside, and transmits / receives the image data to the transmitter / recorder 1.
6 for transmitting encoded data. ROM (Read Only Memory) 12 is an IP
Stores a program for L (Initial Program Loading) and others. RAM (Random Access Memory) 13
Stores a system program (OS (Operating System)) and application programs recorded in the external storage device 15, and stores a CPU (Central Proc.)
The data required for the operation of the essing unit 14 is stored. In accordance with the IPL program stored in the ROM 12, the CPU 14
5, the system program and the application program are expanded in the RAM 13, and the application program is executed under the control of the system program, so that the encoding process described below for the image data supplied from the I / F 11 can be performed. Has been made to do. The external storage device 15 is, for example, a magnetic disk device and stores the system programs and application programs executed by the CPU 14 as described above, and also stores data necessary for the operation of the CPU 14. The transmitter / recording device 16 records encoded data supplied from the I / F 11 on the recording medium 2 or transmits the encoded data via the transmission path 3.

The I / F 11, the ROM 12, the RAM 1
3. The CPU 14 and the external storage device 15 are mutually connected via a bus.

In the transmission device 1 configured as described above, when image data is supplied to the I / F 11, the image data is supplied to the CPU 14. The CPU 14
The image data is encoded, and the resulting encoded data is supplied to the I / F 11. When receiving the encoded data, the I / F 11 supplies the encoded data to the transmitter / recording device 16. In the transmitter / recording device 16, the encoded data from the I / F 11 is recorded on the recording medium 2 or the transmission path 3
Is transmitted via

FIG. 3 is a functional block diagram of a portion of the transmitting device 1 shown in FIG. 2 except for the transmitter / recording device 16.

The image data to be encoded includes a compression unit 21
It is supplied to a local decoding unit 22 and an error calculating unit 23. The compression unit 21 compresses the image data by simply thinning out the pixels, limits the level of the extracted pixel data, and further obtains the resulting compressed data (thinning and level limiting are performed. The subsequent image data) is corrected under the control of the determination unit 24. Compression unit 21
The correction data obtained as a result of the correction is supplied to the local decoding unit 22 and the determination unit 24.

The local decoding section 22 predicts an original image based on the correction data from the compression section 21 and supplies the predicted value to the error calculation section 23. Note that, as described later, the local decoding unit 22 performs a process of obtaining a prediction coefficient for calculating a prediction value by linear combination with correction data, and performs an adaptive process of calculating a prediction value based on the prediction coefficient. As described above, the prediction value is supplied to the error calculation unit 23, and the prediction coefficient obtained at that time is supplied to the determination unit 24, as described above.

The error calculator 23 calculates the prediction error of the prediction value from the local decoder 22 with respect to the original image data (original image) input thereto. This prediction error is supplied to the determination unit 24 as error information.

The determination section 24 converts the correction data output from the compression section 21 based on the error information from the error calculation section 23 into
It is determined whether the encoding result of the original image is appropriate. Then, the determination unit 24 determines whether the compression unit 21
If it is determined that the output of the correction data is not appropriate as the encoding result of the original image, the compression unit 21 is controlled, the compressed data is further corrected, and new correction data obtained as a result is obtained. Is output.
If the determination unit 24 determines that it is appropriate to use the correction data output from the compression unit 21 as the encoding result of the original image, the determination unit 24 optimizes the correction data supplied from the compression unit 21. Multiplexed data (hereinafter, appropriately referred to as optimal compressed data) to the multiplexing unit 25 and the prediction coefficients supplied from the local decoding unit 22 to the multiplexing unit 2.
5.

The multiplexing unit 25 multiplexes the optimum compression data (correction data) from the determination unit 24 and the prediction coefficients, and uses the multiplexed result as encoded data as the transmitter / recording device 16 (FIG. 2). ).

Next, referring to the flowchart of FIG.
The operation will be described. When the image data is supplied to the compression unit 21, the compression unit 21 compresses the image data by thinning out (extracting a small number of pixels from a plurality of pixels) in step S1. Further, the level of the extracted pixel data is limited by extracting two bits on the MSB side in step S2. Then, the pixel data is initially output to the local decoding unit 22 and the determination unit 24 without performing correction. In the local decoding unit 22, in step S3, the correction data from the compression unit 21 (initially, as described above,
The image data is simply thinned out and the level-limited compressed data itself) is locally decoded.

That is, in step S3, a process of obtaining a prediction coefficient for calculating a prediction value of the original image is performed by linear combination with the correction data from the compression unit 21, and the prediction is performed based on the prediction coefficient. An adaptive process for finding a value is performed. The prediction value obtained by the local decoding unit 22 is supplied to an error calculation unit 23, and the prediction coefficient is supplied to a determination unit 24.

Here, the image composed of the predicted values output from the local decoding unit 22 is the same as the decoded image obtained on the receiving device 4 (FIG. 1) side.

The error calculator 23 is provided with the local decoder 2
When the predicted value of the original image is received from Step 2, Step S 4
In, the prediction error of the prediction value from the local decoding unit 22 with respect to the original image data is calculated, and is supplied to the determination unit 24 as error information. Upon receiving the error information from the error calculation unit 23, the determination unit 24 determines in step S5 whether the correction data output from the compression unit 21 is used as the encoding result of the original image based on the error information. Is determined.

That is, in step S5, it is determined whether or not the error information is equal to or less than a predetermined threshold ε. If it is determined in step S5 that the error information is not less than or equal to the predetermined threshold ε, it is recognized that it is not appropriate to use the correction data output from the compression unit 21 as the encoded data of the original image, and the process proceeds to step S6. , The determination unit 24 determines that the compression unit 2
1 to thereby correct the compressed data. The compression unit 21 corrects the compressed data by changing the correction amount (correction value す る described later) under the control of the determination unit 24,
The resulting correction data is sent to the local decoding unit 2
2 and to the determination unit 24. Then, step S3
And the same processing is repeated thereafter.

On the other hand, if it is determined in step S5 that the error information is equal to or smaller than the predetermined threshold ε, the compression unit 21
It is recognized that it is appropriate to use the correction data output by the unit as the encoding result of the original image, and the determination unit 24 converts the correction data obtained when error information equal to or less than the predetermined threshold The data is output to the multiplexing unit 25 together with the prediction coefficients as data. In the multiplexing unit 25, in step S7,
The optimally compressed data and the prediction coefficient from the determination unit 24 are multiplexed, and the encoded data obtained as a result is output, and the process ends.

As described above, when the error information becomes equal to or less than the predetermined threshold value ε, the corrected data obtained by correcting the compressed data is used as the encoding result of the original image.
On the receiving device 4 side, based on the correction data,
An image almost identical to the original image (original image) can be obtained.

FIG. 5 shows an example of the configuration of the compression section 21 shown in FIG.

The image data to be encoded is stored in the thinning circuit 3
0, and is input to the thinning circuit 30.
Is designed to thin out the input image data to 1 / N. Therefore, the thinning circuit 30 outputs compressed data obtained by compressing image data to 1 / N. This compressed data is input to the level limiting circuit 31, and for example, the M of each pixel data represented by 8 bits
By extracting only the two bits on the SB side, the level is limited and then supplied to the correction circuit 32.

The correction circuit 32 gives an address to the correction value ROM 33 in accordance with the control signal from the determination section 24 (FIG. 3), and reads out the correction value △. Then, the correction circuit 32 includes the level limiting circuit 31
For example, by adding a correction value △ from the correction value ROM 33 to the compressed data from, the correction data is generated and supplied to the local decoding unit 22 and the determination unit 24. The correction value ROM 33 stores a combination of various correction values △ (for example, a combination of correction values for correcting one frame of compressed data) for correcting the compressed data output from the level limiting circuit 31. The combination of the correction value △ corresponding to the address supplied from the correction circuit 32 is read and supplied to the correction circuit 32.

Next, referring to FIG. 6, the compression unit 21 shown in FIG.
Will be described.

For example, when image data for one frame or the like is supplied to the thinning circuit 30, the thinning circuit 30
Calculates the image data in step S11 as 1 / N
And outputs the resulting compressed data to the level limiting circuit 31.

Here, as shown in FIG. 7, the thinning circuit 30 thins the image data to, for example, 1/9. That is, the thinning circuit 30 is 3 × 3 (horizontal ×
9 pixels (vertical) are defined as one unit, and only the pixel value of the pixel at the center of each unit (indicated by a black circle in the figure) is extracted, and the other parts (indicated by a circle in the figure) Remove. Note that the thinning circuit 30 performs the above-described processing in units of one frame (field), for example. Therefore, from the thinning circuit 30 to the level limiting circuit 31, one frame of image data is 1 /
9 is supplied as compressed data thinned out. However,
In addition, the thinning process in the thinning circuit 30 may be performed by dividing an image of one frame into several blocks and performing the block unit.

When the level limiting circuit 31 receives the supply of the pixel data thinned out by 1/9 from the thinning circuit 30,
The level is limited by extracting 2 bits from the MSB side of the pixel data represented by 8 bits. That is, each pixel data is represented by two bits, thereby simplifying the subsequent processing.

Upon receiving the compressed data from the level limiting circuit 31, the correction circuit 32 determines in step S13 whether a control signal has been received from the determination unit 24 (FIG. 3). If it is determined in step S13 that the control signal has not been received, the process proceeds to step S16, where the correction circuit 32 converts the compressed data from the level limiting circuit 31 into
The correction data is output as it is to the local decoding unit 22 and the determination unit 24, and the process returns to step S13.

That is, as described above, the determination section 24 controls the compression section 21 (correction circuit 32) based on the error information, and immediately after the compressed data is output from the level limiting circuit 31, Since no error information is obtained yet (because the error information is not output from the error calculation unit 23), no control signal is output from the determination unit 24. For this reason, immediately after the compressed data is output from the level limiting circuit 31, the correction circuit 32 does not correct the compressed data (corrects by adding 0), and uses the local decode unit 22 and Output to the determination unit 24.

On the other hand, in step S13, the judgment unit 2
If it is determined that the control signal from the control signal 4 has been received, the correction circuit 32 outputs the address according to the control signal to the correction value ROM 33 in step S14. Thereby, in step S14, a combination (set) of correction values △ for correcting one frame of compressed data stored at the address is read from the correction value ROM 33 and supplied to the correction circuit 32. You. Correction circuit 32
Receives the combination of the correction values か ら from the correction value ROM 33, adds the corresponding correction value に to each of the compressed data of one frame in step S15,
Correction data obtained by correcting the compressed data is calculated. Thereafter, the process proceeds to step S16, in which the correction data is
2 to the local decoding unit 22 and the determination unit 24, and the process returns to step S13.

As described above, under the control of the determination unit 24, the compression unit 21 repeatedly outputs the corrected data obtained by correcting the compressed data to various values.

When the coding of one frame image is completed, the judging unit 24 supplies a control signal indicating this to the compressing unit 21. It is also determined whether such a control signal has been received. If it is determined in step S13 that a control signal indicating that encoding of one frame of image has been completed has been received, the process returns to step S11 after waiting for the supply of the image of the next frame, and returns to step S11. Is repeated.

Further, in the above-described case, the compressed data is generated by causing the thinning circuit 30 to extract only the pixel data (pixel value) of the center pixel of the 3 × 3 pixels. For example, it is also possible to calculate the average value of 3 × 3 pixels and generate the compressed data using the average value as the pixel value of the pixel at the center of the 3 × 3 pixels.

FIG. 8 shows an example of the configuration of the local decoding section 22 shown in FIG.

The correction data from the compression unit 21 is supplied to a class classification blocking circuit 41 and a predicted value calculation blocking circuit 42. The class classification blocking circuit 41 is a unit for classifying the correction data into a predetermined class according to the property thereof, and is a unit for class classification centering on the target correction data (pixel) (target correction data). It is made into blocks.

That is, in FIG. 7, if the i-th correction data (compressed data) (or pixel) (or pixel) (the portion indicated by a black circle in the figure) from the top is represented by _Xij , The classifying blocking circuit 41 includes four pixels X _{(i-1) j} , X _{i (j-1)} , and X _i adjacent to the pixel of interest (correction data of interest) X _ij above, left, right, and below. _{(j + 1)} and X _{(i-1) j} , including the self, constitute a class classification block composed of a total of 5 pixels. The classification block is supplied to the classification adaptive processing circuit 43.

In this case, the class classification block is composed of blocks composed of pixels arranged in a cross shape, but the shape of the class classification block does not need to be a cross shape. In addition, for example, the shape can be a rectangle, a square, or any other shape. Also, the number of pixels that make up the class classification block is
It is not limited to five pixels.

The prediction value calculation blocking circuit 42 blocks the correction data into a prediction value calculation block centering on the target correction data, which is a unit for calculating the prediction value of the original image. It has been done. In other words, now, in FIG. 7, the correction data X _ij (the part indicated by the mark ● in the figure)
3 × 3 9 in the original image (original image)
The pixel value of a pixel is calculated from the leftmost to the right and from the top down to _Yij (1), _Yij (2), _Yij (3), Y
_ij (4), Y _ij (5), Y _ij (6), Y _ij (7), Y _ij
(8) and Y _ij (9), the prediction value calculation blocking circuit 42 calculates the prediction values of the pixels Y _ij (1) to Y _ij (9) by, for example, the pixel X _ij 5 around
× 5 25 pixels X _{(i-2) (j-2)} , X _{(i-2) (j-1)} ,
X _{(i-2) j} , X _{(i-2) (j + 1)} , X _{(i-2) (j + 2)} ,
X _{(i-1) (j-2)} , X _{(i-1) (j-1)} , X _{(i-1) j} ,
X _{(i-1) (j + 1)} , X _{(i-1) (j + 2)} , Xi _(j-2) , Xi _(j-1) , X
_ij , Xi _{(j + 1)} , Xi _{(j + 2)} , X _{(i + 1) (j-2)} ,
X _{(i + 1) (j-1)} , X _{(i + 1) j} , X _{(i + 1) (j + 1)} ,
X _{(i + 1) (j + 2)} , X _{(i + 2) (j-2)} , X _{(i + 2 ) (j-1)} ,
X _{(i + 2) j} , X _{(i + 2) (j + 1)} , and X _{(i + 2) (j + 2)} constitute a square-like predicted value calculation block. I have.

Specifically, for example, pixels Y ₃₃ (1) to Y ₃₃ in the original image, which are surrounded by a square in FIG.
_{33 For} the calculation of the predicted value of (9), the pixels X ₁₁ , X ₁₂ ,
_{X 13, X 14, X 15} , X 21, X 22, X 23, X 24, X 25, X
₃₁ , _X32 , _X33 , _X34 , _X35 , _X41 , _X42 , _X43 ,
_X44 , _X45 , _X51 , _X52 , _X53 , _X54 , and _{X55 form} a prediction value calculation block.

The predicted value calculation block obtained by the predicted value calculation blocking circuit 42 is supplied to a classification adaptive processing circuit 43.

Note that the predicted value calculation block is also
As in the case of the class classification block, the number and shape of the pixels are not limited to those described above. However, the number of pixels constituting the predicted value calculation block is
It is desirable that the number of pixels is larger than the number of pixels constituting the class classification block.

In the case of performing the above-described blocking (the same applies to processing other than blocking), there is a case where there is no corresponding pixel near the image frame of the image. In this case, for example, The processing is performed assuming that the same pixel as the pixel forming the image frame exists outside the pixel.

The class classification adaptive processing 43 is composed of a class classification circuit 45 and an adaptive processing 46, and performs a class classification adaptive processing.

The class classification adaptive process is a process of classifying an input signal into several classes based on the characteristics thereof and performing an appropriate adaptive process on the input signal of each class for the class. It is divided into processing and adaptive processing.

Here, the classification process and the adaptation process will be briefly described.

First, the class classification processing will be described.

Now, for example, as shown in FIG.
By a certain pixel of interest and three pixels adjacent thereto, 2
A block (class classification block) composed of × 2 pixels is formed, and each pixel is represented by 1 bit (takes any level of 0 or 1). In this case, as shown in FIG. 9B, a block of 4 pixels of 2 × 2 has 16 (=
(2 ¹ ) ⁴ ) Can be classified into patterns. Such pattern division is class classification processing, and is performed in the class classification circuit 45.

The class classification process can be performed in consideration of the activity (complexity of the image) (the degree of change) of the image (the image in the block).

Here, usually, for example, about 8 bits are assigned to the original pixel. Further, in the present embodiment, as described above, the class classification block includes five pixels. Therefore, if such a class classification block is composed of the original (8-bit) pixels and the class classification processing is performed, the blocks are classified into an enormous number of (2 ⁸ ) ⁵ classes.

On the other hand, in the present embodiment, as described above, the number of bits of pixels is reduced (from 8 bits to 2 bits) in the level limiting circuit 31, thereby reducing the number of classes. (1024 (=
(2 ² ) ⁵ )).

Next, the adaptive processing will be described.

For example, the predicted value E [y] of the pixel value y of the original image is now converted into the pixel values of some pixels around it (hereinafter, appropriately referred to as learning data) x ₁ , x ₂ ,. .. And a linear first-order combination model defined by a linear combination of predetermined prediction coefficients w ₁ , w ₂ ,... In this case, the predicted value E [y] can be expressed by the following equation.

E [y] = w ₁ x ₁ + w ₂ x ₂ +... + W ₂₅ x ₂₅ + w ₀ (1) where w ₀ is originally 8-bit data x _i and lower 6 bits
Since the bits were deleted and converted to 2-bit data,
It is a so-called offset term (offset coefficient) corresponding to the lower 6 bits added to express E [y] with 8 bits. Since the offset coefficient w ₀ is a coefficient for obtaining a predicted value, like the prediction coefficients w ₁ , w ₂ ,..., The offset coefficient w ₀ will be appropriately included in the prediction coefficients. However, here, for simplicity of explanation, the adaptive processing will be described ignoring the offset coefficient w ₀ .

Now, a matrix W composed of a set of prediction coefficients w, a matrix X composed of a set of learning data, and a matrix Y ′ composed of a set of predicted values E [y] are

(Equation 1) Defines the following observation equation.

XW = Y ′ (2)

Then, it is considered that a least square method is applied to this observation equation to obtain a predicted value E [y] close to the pixel value y of the original image. In this case, a matrix Y composed of a set of pixel values y of the original image (hereinafter, appropriately referred to as teacher data),
And a matrix E consisting of a set of residuals e of the predicted values E [y] for the pixel values y of the original image,

(Equation 2) From equation (2), the following residual equation is established.

XW = Y + E (3)

In this case, the prediction coefficient w _i for obtaining a prediction value E [y] close to the pixel value y of the original image is a square error

(Equation 3) Can be obtained by minimizing.

Therefore, when the above-described squared error is differentiated by the prediction coefficient w _i to be 0, that is, the prediction coefficient w _i that satisfies the following equation is a prediction value E [y that is close to the pixel value y of the original image. ]
Is the optimum value.

[0102]

(Equation 4) ... (4)

Therefore, first, the equation (3) is calculated using the prediction coefficient w _i
By differentiating with, the following equation is established.

[0104]

(Equation 5) ... (5)

From equations (4) and (5), equation (6) is obtained.

[0106]

(Equation 6) ... (6)

Further, the learning data x, the prediction coefficient w, the teacher data y, and the residual e in the residual equation of the equation (3) are obtained.
In consideration of the relationship, the following normal equation can be obtained from Expression (6).

[0108]

(Equation 7) ... (7)

The normal equation of equation (7) can be set by the same number as the number of prediction coefficients w to be obtained. Therefore, by solving equation (7), the optimum prediction coefficient w can be obtained. . In solving equation (7), for example, a sweeping-out method (Gauss-Jordan elimination method) or the like can be applied.

By extending the above description to include the offset coefficient w ₀ , the optimum offset coefficient w ₀ can be obtained together with the prediction coefficient.

As described above, the optimum prediction coefficient w is determined for each class, and the prediction coefficient w close to the pixel value y of the original image is calculated by the equation (1) using the prediction coefficient w.
[Y] is obtained by adaptive processing.
The processing is performed in the adaptive processing circuit 46.

Note that the adaptive processing differs from the interpolation processing in that components included in the original image that are not included in the thinned image are reproduced. That is, the adaptive processing is the same as the interpolation processing using a so-called interpolation filter as far as only the equation (1) is viewed, but the prediction coefficient w corresponding to the tap coefficient of the interpolation filter is calculated using the teacher data y. In other words, since it is determined by learning, the components contained in the original image can be reproduced. From this, it can be said that the adaptive processing has a so-called image creation action.

Next, the processing of the local decoding unit 22 in FIG. 8 will be described with reference to the flowchart in FIG.

In the local decoding unit 22, first, in step S21, the correction data from the compression unit 21 is divided into blocks. That is, in the class classification block forming circuit 41, the correction data is divided into a class classification block composed of five pixels centering on the target correction data (FIG. 7) and supplied to the class classification adaptive processing circuit 43. At the same time, in the prediction value calculation blocking circuit 42, the correction data is divided into prediction value calculation blocks composed of 5 × 5 pixels centered on the target correction data, and supplied to the class classification adaptive processing circuit 43. Is done.

As described above, the class classification adaptive processing circuit 43 is supplied with the original image data in addition to the class classification block and the predicted value calculation block. Classification circuit 45
In addition, the prediction value calculation block and the original image data are supplied to the adaptive processing circuit 46.

In step S22, the class classification circuit 45 performs a class classification process on the class classification block. That is, the state of the level distribution of each pixel constituting the classifying block is detected, and the class to which the classifying block belongs (the class of the target correction data constituting the classifying block) is determined. The result of this class determination is supplied to the adaptive processing circuit 46 as class information.

In the present embodiment, since the class classification processing is performed on the class classification block composed of five pixels represented by 2 bits, each class classification block is 1024 (blocks). = (2 ² ) ⁵ ).

Then, the process proceeds to a step S23, where the adaptive processing circuit 46 performs an adaptive process for each class on the basis of the class information from the class classification circuit 45. Of the original image data is calculated.

That is, in the present embodiment, for example,
A 26 × 9 prediction coefficient for each class is calculated from the original image and the correction data for each frame. Then, when attention is paid to a certain correction data, prediction is performed on a total of nine pixels of pixels of the original image corresponding to the correction data of interest and eight pixels of the original image adjacent to the pixel. Using a 5 × 5 prediction value calculation block having 25 × 5 pixels centered on the target correction data and 26 × 9 prediction coefficients corresponding to the class information, the linear primary expression of the equation (1) is obtained. It is calculated by calculating the formula.

Specifically, for example, five correction data X ₂₃ , X ₂₃ centered on the correction data X ₃₃ shown in FIG.
The class information C about the class classification block composed of ₃₂ , X ₃₃ , X ₃₄ , and X ₄₃ (the class classification block using the correction data X ₃₃ as the target correction data) is stored in the class classification circuit 45.
And correction data X ₁₁ , X ₁₂ , X ₁₃ , X of 5 × 5 pixels centered on the correction data X ₃₃ as prediction value calculation blocks corresponding to the class classification block.
_{14, X 15, X 21,} X 22, X 23, X 24, X 25, X 31,
_X32 , _X33 , _X34 , _X35 , _X41 , _X42 , _X43 , _X44 , X
_A prediction value calculation block consisting of ₄₅ , X ₅₁ , X ₅₂ , X ₅₃ , X ₅₄ , and X ₅₅ (a prediction value calculation block in which the correction data X ₃₃ is the target correction data) is used as a prediction value calculation block circuit 4.
Assuming that output from the 2, first, the correction data for constituting the predicted values calculating block, with the learning data, in the original image, 3 × 3 pixels around the correction data X ₃₃ (FIG. 7 , The pixel values Y ₃₃ (1) to Y ₃₃ (9) of the portion surrounded by a square are used as teacher data to form a normal equation corresponding to the equation (7).

Further, a normal equation is similarly set for other prediction value calculation blocks classified into the same class information C within one frame as a predetermined period, for example. Y ₃₃ (k) (here, k =
The prediction coefficients w ₁ (k) to w ₂₅ (k) for obtaining the prediction values E [Y ₃₃ (k)] of (1, 2,..., 9) (in this embodiment, one prediction value is 25 learning data to find
Are used, and one offset coefficient is required, and accordingly, 26 predictive coefficients w are required correspondingly). (Thus, until such a number of normal equations are obtained, in step S23, a process of establishing a normal equation is performed.) By solving the normal equation, the pixel value Y ₃₃ ( Twenty-six optimal prediction coefficients (including offset coefficients) w ₀ (k) to w ₂₅ (k) for obtaining the predicted value E [Y ₃₃ (k)] of k) are calculated (25 correction data items). Is used to obtain nine prediction values, the number of prediction coefficients for one class is
26 × 9, which is the sum of 25 × 9 and 9 offset coefficients that correspond to the 9 predicted values).

This process is performed for each class, and 26 × 9 prediction coefficients are calculated for each class. Then, 26 × 9 prediction coefficients corresponding to the class information C,
The predicted value E [Y ₃₃ (k)] is obtained using the predicted value calculation block and the following expression corresponding to Expression (1).

E [Y ₃₃ (k)] = w ₁ (k) X ₁₁ + w ₂ (k) X ₁₂ + w ₃ (k) X ₁₃ + w ₄ (k) X ₁₄ + w ₅ (k) X ₁₅ + w ₆ ( _{k) X 21 + w 7 (} k) X 22 + w 8 (k) X 23 + w 9 (k) X 24 + w 10 (k) X 25 + w 11 (k) X 31 + w 12 (k) X 32 + w 13 (k ) X ₃₃ + w ₁₄ (k) X ₃₄ + w ₁₅ (k) X ₃₅ + w ₁₆ (k) X ₄₁ + w ₁₇ (k) X ₄₂ + w ₁₈ (k) X ₄₃ + w ₁₉ (k) X ₄₄ + w ₂₀ (k) X ₄₅ + w ₂₁ (k) X ₅₁ + w ₂₂ (k) X ₅₂ + w ₂₃ (k) X ₅₃ + w ₂₄ (k) X ₅₄ + w ₂₅ (k) X ₅₅ + w ₀ (k) (8)

In step S23, as described above,
A 26 × 9 prediction coefficient is obtained for each class, and a prediction value of a pixel of a 3 × 3 original image centering on the correction data of interest is obtained using the prediction coefficient for each class.

Thereafter, the flow advances to step S24, where the 26 × 9 prediction coefficients for each class are supplied to the determination unit 24,
The prediction value for one frame obtained in units of three pixels is supplied to the error calculation unit 23. Then, the process returns to step S21, and the same process is repeated for each frame as described above, for example.

Next, FIG. 11 shows an example of the configuration of the error calculator 23 in FIG.

The original image data is supplied to the blocking circuit 51. The blocking circuit 51 converts the image data to the predicted value output from the local decoding unit 22. A block of 3 × 3 pixels (for example, a block of 3 × 3 pixels surrounded by a square in FIG. 7) obtained as a result is output to the square error calculation circuit 52. It has been done. As described above, the square error calculation unit 52
In addition to the blocks being supplied from the blocking circuit 51, the predicted values are supplied in units of nine (in units of 3 × 3 pixels) from the local decoding unit 22.
The square error calculation circuit 52 calculates a square error as a prediction error of a predicted value with respect to the original image, and supplies the calculated square error to the integrating unit 55.

That is, the square error calculation circuit 52 is composed of arithmetic units 53 and 54. The computing unit 53 subtracts a corresponding predicted value from each of the blocked image data from the blocking circuit 51 and supplies the subtracted value to the computing unit 54. Arithmetic unit 54
Is configured to square the output of the arithmetic unit 53 (the difference between the original image data and the predicted value) and supply it to the integrating unit 55.

When receiving the square error from the square error calculating circuit 52, the integrating section 55 reads the stored value of the memory 56, adds the stored value and the square error, and supplies the stored value to the memory 56 again for storage. By repeating this, the integrated value (error variance) of the square error is obtained. Further, when the integration of the square error for a predetermined amount (for example, for one frame) is completed, the integrating unit 55 reads out the integrated value from the memory 56 and supplies the integrated value to the determining unit 24 as error information. ing. Each time the processing for one frame is completed, the memory 56
The output value of the integrating unit 55 is stored while clearing the stored value.

Next, the operation will be described with reference to the flowchart of FIG. In the error calculator 23, first, in step S31, the stored value of the memory 56 is cleared to, for example, 0, and the process proceeds to step S32.
In the blocking circuit 51, the image data is blocked as described above, and the resulting block is
It is supplied to the square error calculation circuit 52. In the square error calculation circuit 52, in step S33, the blocking circuit 5
The square error between the image data of the original image constituting the block supplied from 1 and the predicted value supplied from the local decoding unit 22 is calculated.

That is, in step S 33, the corresponding predicted value is subtracted from each of the blocked image data supplied from the blocking circuit 51 in the computing unit 53, and the result is supplied to the computing unit 54. Further, step S3
In 3, the output of the arithmetic unit 53 is squared in the arithmetic unit 54 and supplied to the integrating unit 55.

Upon receiving the squared error from the squared error calculation circuit 52, the integrating unit 55 reads the stored value of the memory 56 in step S34 and adds the stored value and the squared error to obtain the integrated value of the squared error. Ask for. Accumulator 5
5 is stored in the memory 56
And is stored by overwriting the previous stored value.

Then, in the integrating section 55, step S35
In, it is determined whether or not the integration of the square error for a predetermined amount, for example, for one frame is completed. If it is determined in step S35 that the integration of the square error for one frame has not been completed, the process returns to step S32, and the processing from step S32 is repeated again. If it is determined in step S35 that the integration of the square error for one frame has been completed, the process proceeds to step S36, where the integration unit 55
Then, the integrated value of the squared error for one frame stored in 6 is read out and output to the determination unit 24 as error information. Then, the process returns to step S31, and the process from step S31 is repeated again for the next frame.

Therefore, the error calculating section 23 sets the original image data to Y _ij (k) and sets the predicted value to E [Y
_ij (k)], error information Q is calculated by performing an operation according to the following equation.

Q = {(Y _ij (k) −E [Y _ij (k)]) ² Here, Σ means summation for one frame.

Next, FIG. 13 shows an example of the configuration of the determination section 24 of FIG.

The prediction coefficient memory 61 stores the prediction coefficients supplied from the local decoding section 22. The correction data memory 62 is configured to store the correction data supplied from the compression unit 21.

It should be noted that the correction data memory 62 is
In step 1, when the compressed data is newly corrected, and new correction data is supplied, new correction data is stored in place of the already stored correction data (previous correction data). It has been made like that. Also,
As described above, at the timing when the correction data is updated to a new one, the local decoding unit 22 outputs a new set of prediction coefficients for each class corresponding to the new correction data. When the new prediction coefficient for each class is also supplied to the memory 61 in this way, the new prediction coefficient for each class (the previous prediction coefficient for each class) is replaced with the new prediction coefficient for each class. The prediction coefficient for each is stored.

The error information memory 63 stores the error information supplied from the error calculator 23.
The error information memory 63 stores the error information supplied last time in addition to the error information supplied this time from the error calculation unit 23 (even if new error information is supplied, Until new error information is provided,
The error information already stored is retained). Note that the error information memory 63 is cleared each time processing for a new frame is started.

The comparison circuit 64 compares the current error information stored in the error information memory 63 with a predetermined threshold value ε.
Further, if necessary, the current error information is compared with the previous error information. The comparison result in the comparison circuit 64 is supplied to the control circuit 65.

The control circuit 65 determines the suitability (optimality) of making the correction data stored in the correction data memory 62 the coding result of the original image based on the comparison result of the comparison circuit 64. When it is recognized (determined) that the data is not optimal, a control signal requesting the output of new correction data is supplied to the compression unit 21 (correction circuit 32) (FIG. 5). When the control circuit 65 recognizes that it is optimal to use the correction data stored in the correction data memory 62 as the encoding result of the original image, the control circuit 65 stores the class data stored in the prediction coefficient memory 61. In addition to reading out the prediction coefficient for each and outputting it to the multiplexing unit 25, the correction data stored in the correction data memory 62 is read out and supplied to the multiplexing unit 25 as optimal compressed data. Further, in this case, the control circuit 65 outputs a control signal indicating that encoding of the image of one frame has been completed to the compression unit 21, and as described above, the control unit 65 supplies the next signal to the compression unit 21. The processing for a frame is started.

Next, the operation of the determination section 24 will be described with reference to FIG. In the determination unit 24, first, in step S41, the comparison circuit 64 determines whether or not error information has been received from the error calculation unit 23.
If it is determined that the error information has not been received, the process returns to step S41. If it is determined in step S41 that the error information has been received, that is, if the error information has been stored in the error information memory 63, the process proceeds to step S42, where the comparison circuit 64 stores the error information in the error information memory 63. The obtained error information (current error information) is compared with a predetermined threshold value ε to determine which is larger.

If it is determined in step S42 that the current error information is equal to or greater than the predetermined threshold value ε, the comparison circuit 64 reads the previous error information stored in the error information memory 63. Then, the comparison circuit 64
In step S43, the previous error information is compared with the current error information to determine which is larger.

Here, when the processing for one frame is started and error information is first supplied, the error information memory 63 does not store the previous error information. In step 24, step S
The processing after step 43 is not performed, and the control circuit 65 outputs a predetermined initial address (an address in the correction value ROM 33 (FIG. 5) in which the initial value of the correction value is stored) so as to output the predetermined initial address. The control signal for controlling 5) is output.

If it is determined in step S43 that the current error information is equal to or less than the previous error information, that is, if the error information is reduced by performing correction of the compressed data, the process proceeds to step S44, and the control circuit proceeds to step S44. 65 outputs a control signal instructing to change the correction value △ in the same manner as the previous time, to the correction circuit 32, and returns to step S41. If it is determined in step S43 that the current error information is larger than the previous error information, that is, if the error information increases by performing the correction of the compressed data, the process proceeds to step S45, and the control circuit 65 , A control signal for instructing the correction value △ to be changed in the opposite direction to the previous time is output to the correction circuit 32, and the process returns to step S41.

When the error information, which has been decreasing, starts to increase at a certain timing, the control circuit 6
Reference numeral 5 denotes a control signal for instructing the correction value で to be changed, for example, by half the size of the previous case, in the opposite manner to the previous time.

By repeating the processing of steps S41 to S45, the error information decreases. As a result, if it is determined in step S42 that the current error information is smaller than the predetermined threshold value ε, the process proceeds to step S46.
The control circuit 65 reads the prediction coefficient for each class stored in the prediction coefficient memory 61, reads the correction data for one frame stored in the correction data memory 62 as optimal compressed data, and performs multiplexing. Part 2
5 and the process ends.

After that, the processing according to the flowchart shown in FIG. 14 is repeated again after the error information for the next frame is supplied.

It is to be noted that the correction circuit 32 can correct the compressed data for all the compressed data of one frame, or can perform the correction for only a part of the compressed data. . In the case where correction is performed only for a part of the compressed data, the control circuit 65 can be made to detect, for example, a pixel having a strong influence on the error information, and correct only the compressed data for such a pixel. . Pixels having a strong influence on error information can be detected, for example, as follows. That is, first, the error information is obtained by performing the processing using the level-controlled compressed data of the pixels remaining after the thinning as it is. Then, a control signal for causing the compressed data to be processed one by one by the same correction value △ is output from the control circuit 65 to the correction circuit 32, and the resulting error information is Is compared with the error information obtained when using
A pixel whose difference is equal to or larger than a predetermined value may be detected as a pixel having a strong influence on error information.

As described above, the correction of the compressed data is repeated until the error information is made smaller than the predetermined threshold ε (below), and the correction data when the error information becomes smaller than the predetermined threshold ε is: Since the image data is output as a result of encoding the image, the receiving device 4 (FIG. 1) corrects the pixel values of the pixels constituting the decimated image to the most appropriate values for restoring the original image. Accordingly, it is possible to obtain a decoded image (high-quality decoded image) that is the same (almost the same) as the original image.

Further, since the image is compressed not only by the thinning process but also by the level limiting process, the class classification adaptive process, etc., it is possible to obtain encoded data having a very high compression ratio. The above-described encoding processing in the transmission device 1 realizes high-efficiency compression by using the compression processing by thinning and the classification adaptive processing in an organically integrated manner. From this, it can be said that it is an integrated coding process.

Next, FIG. 15 shows a configuration example of the receiving device 4 of FIG.

In the receiver / reproducing device 71, the encoded data recorded on the recording medium 2 is reproduced, or the encoded data transmitted via the transmission path 3 is received.
It is supplied to the separation unit 72. The separating unit 72 sequentially separates the correction data for one frame and the prediction coefficient for each class from the encoded data, and the correction data for one frame is divided into a class classification blocking circuit 73 and a prediction value calculation blocking circuit. And the prediction coefficient for each class is
Supplied to the prediction circuit 76, and the built-in memory 76A
Is stored.

Classification blocking circuit 73, class classification circuit 75, or predicted value calculation blocking circuit 77
Has the same configuration as the class classification blocking circuit 41, the class classification circuit 45, or the predicted value calculation blocking circuit 42 in FIG. 8, and therefore, in these blocks, the same as in FIG. Is performed, whereby the prediction value calculation blocking circuit 7
7 outputs a prediction value calculation block, and the class classification circuit 75 outputs class information. These prediction value calculation block and class information are supplied to the prediction circuit 76.

In the prediction circuit 76, the prediction coefficient corresponding to the class information is read out of the prediction coefficients for each class stored in the built-in memory 76A, and the prediction coefficient and the prediction value calculation blocking circuit are read out. A prediction value is calculated according to equation (1) using the correction data constituting the prediction value calculation block supplied from 77, and an image of one frame composed of such prediction values is output as a decoded image. Is done. This decoded image is almost the same as the original image, as described above.

On the receiving side, even if the receiving apparatus is not the receiving apparatus 4 as shown in FIG. 15, the apparatus for decoding the decimated image by simple interpolation can be used without using prediction coefficients.
A decoded image can be obtained by performing normal interpolation.
However, the decoded image obtained in this case has image quality (resolution)
Is deteriorated.

In the above-described case, the prediction coefficient is calculated in the local decoding unit 22 in FIG. 3 and the prediction value is calculated using the prediction coefficient. It can be calculated (using a prediction coefficient obtained in advance by learning).

That is, FIG. 16 shows the second example of the transmitting apparatus 1 shown in FIG.
2 shows an example of a functional configuration. Note that, in the figure, parts corresponding to those in FIG. 3 are denoted by the same reference numerals. That is, the transmitting device 1 includes a local decoding unit 2
The configuration is the same as that in FIG. 3 except that a local decoding unit 1022 is provided instead of 2.

However, in FIG. 3, the original image data is supplied to the local decoding unit 22, but in FIG. 16, the original image data is not supplied to the local decoding unit 1022. ing.

FIG. 17 is a block diagram of the local decoding unit 1 shown in FIG.
022 shows a configuration example. Note that, in the figure, parts corresponding to those in FIG. 8 are denoted by the same reference numerals. That is, the local decoding unit 1022 is configured similarly to the local decoding unit 22 in FIG. 8 except that a prediction coefficient ROM 81 and a prediction circuit 82 are provided instead of the adaptive processing circuit 46.

The prediction coefficient ROM 81 stores 26 × 9 prediction coefficients for each class obtained by performing learning (described later) in advance.
5 receives the class information output, reads the prediction coefficient stored at the address corresponding to the class information, and supplies the prediction coefficient to the prediction circuit 82.

The prediction circuit 82 uses the prediction value calculation block of 5 × 5 pixels from the prediction value calculation blocking circuit 42 and the 26 × 9 prediction coefficient from the prediction coefficient ROM 81 to obtain the equation (1). (Specifically, for example, a linear linear expression shown in Expression (8) is calculated, and thereby, a predicted value of 3 × 3 pixels of the original image is calculated.

Therefore, according to the classification adaptive processing circuit 43 shown in FIG. 17, the predicted value is calculated without using the original image (original image). For this reason, as described above, the original image is not supplied to the local decoding unit 1022 (there is no need to supply it, so it is not supplied).

Next, the processing of the local decoding unit 1022 in FIG. 17 will be further described with reference to the flowchart in FIG.

In local decoding section 1022,
First, in steps S1021 and S1022, the same processes as those in steps S21 and S22 in FIG. 10 are performed, whereby the class information is output from the class classification circuit 45. This class information is supplied to the prediction coefficient ROM 81. Further, the prediction value calculation block is output from the prediction value calculation blocking circuit 42 and supplied to the prediction circuit 82.

Upon receiving the class information, the prediction coefficient ROM 81 reads out a 26 × 9 prediction coefficient corresponding to the class information from among the stored prediction coefficients for each class, and instructs the prediction circuit 82 in step S1023. Supply.

The prediction circuit 82 is supplied with prediction coefficients of 26 × 9 from the prediction coefficient ROM 81, and also receives a prediction value calculation block of 5 × 5 pixels from the prediction value calculation blocking circuit 42 as described above. Supplied. Then, in the prediction circuit 82, in step S1024, the prediction coefficient RO
The adaptive processing is performed using the 26 × 9 prediction coefficient from M81 and the prediction value calculation block of 5 × 5 pixels from the prediction value calculation blocking circuit 42, that is, specifically, By performing the calculation according to Expression (1) (or Expression (8)), the correction data of interest (here,
The predicted value of the pixel of the 3 × 3 original image centered on the pixel at the center of the predicted value calculation block) is obtained.

Thereafter, for example, when a predicted value for one frame is obtained, the process proceeds to step S1025, where the prediction coefficient R
The 26 × 9 prediction coefficient for each class stored in the OM 81 is read and supplied to the determination unit 24, and the prediction value obtained in step S1024 is supplied to the error calculation unit 23. Then, the process returns to step S1021, and thereafter, the same processing is repeated, for example, in units of one frame.

In this embodiment, the prediction coefficient for each class is the one stored in the prediction coefficient ROM 81, and its value does not change.
In 1025, the prediction coefficient for each class is determined by the determination unit 2
After once being supplied to 4, there is basically no need to supply again.

Also, even when the transmitting device 1 is configured as shown in FIG. 16, the receiving device 4 can decode a substantially same decoded image as the original image by configuring as shown in FIG. Obtainable.

Next, FIG. 19 shows the prediction coefficient ROM of FIG.
8 shows a configuration example of an image processing apparatus that performs learning for obtaining a prediction coefficient stored in 81.

The learning blocking circuit 91 and the teacher blocking circuit 92 have learning image data (learning image data) for obtaining prediction coefficients applicable to any image (thus, before thinning processing). ) Are supplied.

The learning blocking circuit 91 calculates the center of the pixel of interest from the input image data.
Then, 25 pixels (5 × 5 pixels) having the positional relationship indicated by the mark ● are extracted (in this case, the pixel of interest is X ₃₃ ).
A block composed of pixels is used as a learning block.
It is supplied to the level limiting circuit 93. Level limiting circuit 93
Restricts the level of the input pixel data and outputs it to the classifying circuit 94 and the learning data memory 96.

In the teacher blocking circuit 92, a block composed of, for example, 3 × 3 9 pixels centering on the target pixel is generated from the input image data.
The block composed of the nine pixels is supplied to the teacher data memory 98 as a teacher block.

When the learning block forming circuit 91 generates, for example, a learning block composed of 25 pixels having a positional relationship indicated by a circle in FIG. 3x shown in the figure with a square
A teacher block of three pixels is generated.

In the class classification circuit 94, the central nine pixels (3 × 3 pixels) are extracted from the 25 pixels constituting the learning block.
Classification is given. Then, the obtained class information is supplied to the learning data memory 96 and the teacher data memory 98 via the terminal a of the switch 95.

In the learning data memory 96 or the teacher data memory 98, an address (AD) corresponding to the class information supplied thereto is added to a learning block from the level limiting circuit 93 or a teacher block from the teacher blocking circuit 92. Each block is stored.

Therefore, in the learning data memory 96,
For example, if a block composed of 5 × 5 pixels indicated by a mark in FIG. 7 is stored as a learning block at a certain address, in the teacher data memory 98, the same address as that address is used. A block of 3 × 3 pixels surrounded by a rectangle is stored as a teacher block.

Hereinafter, the same processing is repeated for all learning images prepared in advance, whereby the learning block and the local decoding unit 1 shown in FIG.
022, a teacher block composed of 9 pixels for which a predicted value is obtained using a predicted value calculation block composed of 25 correction data having the same positional relationship as the 25 pixels constituting the learning block; Are stored at the same address in the learning data memory 96 and the teacher data memory 98.

In the learning data memory 96 and the teacher data memory 98, a plurality of pieces of information can be stored at the same address, whereby a plurality of learning blocks can be stored at the same address. And a teacher block can be stored.

When the learning blocks and the teacher blocks for all the learning images are stored in the learning data memory 96 and the teacher data memory 98, the switch 95 that has selected the terminal a is switched to the terminal b. This allows
The output of the counter 97 is supplied to the learning data memory 96 and the teacher data memory 98 as an address. The counter 97 counts a predetermined clock and outputs the count value. In the learning data memory 96 or the teacher data memory 98, a learning block or a teacher block stored at an address corresponding to the count value is stored. The data is read out and supplied to the arithmetic circuit 99.

Therefore, the arithmetic circuit 99 includes the counter 97
, A set of learning blocks of a class corresponding to the count value and a set of teacher blocks are supplied.

When the arithmetic circuit 99 receives the set of learning blocks and the set of teacher blocks for a certain class, it calculates a prediction coefficient that minimizes the error by using, for example, the least square method. I do.

That is, for example, let the pixel values of the pixels constituting the learning block be x ₁ , x ₂ , x ₃ ,.
When the prediction coefficients to be obtained are w ₀ , w ₁ , w ₂ , w ₃ ,..., To obtain the pixel value y of a certain pixel constituting the teacher block by using these linear linear combinations, The prediction coefficients w ₀ , w ₁ , w ₂ , w ₃ ,... Must satisfy the following equation corresponding to equation (1).

Y = w ₁ x ₁ + w ₂ x ₂ + w ₃ x ₃ +... + W ₀

Therefore, in the arithmetic circuit 99, the prediction value w ₁ x ₁ + w ₂ x ₂ + w ₃ x ₃ +... With respect to the true value y is obtained from the learning block of the same class and the corresponding teacher block.
Prediction coefficients w ₁ , w ₂ , w ₃ , which minimize the square error of + w ₀ ,
.., W ₀ are obtained by setting up and solving a normal equation corresponding to the above equation (7).

The prediction coefficients for each class obtained in the arithmetic circuit 99 are supplied to the memory 100. The memory 100 is supplied with the count value from the counter 97 in addition to the prediction coefficient from the arithmetic circuit 99,
In the memory 100, the prediction coefficient from the arithmetic circuit 99 is stored at an address corresponding to the count value from the counter 97.

As described above, in the memory 100, at the address corresponding to each class, the optimum prediction coefficient for predicting the pixels of the block of the class is stored.

The prediction coefficient ROM 81 of FIG. 17 stores the prediction coefficient stored in the memory 100 as described above.

Next, FIG. 20 shows a third example of the transmitting apparatus 1 shown in FIG.
2 shows an example of a functional configuration. Note that, in the figure, parts corresponding to those in FIG. 3 are denoted by the same reference numerals. That is, the transmitting device 1 includes a local decoding unit 2
2 or the local decoding unit 20 instead of the determination unit 24
The configuration is the same as that of FIG. 3 except that the multiplexing unit 25 is not provided while the multiplexing unit 25 and the determination unit 2024 are provided.

However, also in this case, as in the case of FIG. 16, the original image data is not supplied to the local decode unit 2022. Further, in the embodiment of FIG. 20, the local decoding unit 2022 does not output the prediction coefficient to the determination unit 2024.

Next, the operation will be described with reference to the flowchart of FIG.

When the image data is supplied to the compression unit 21, the compression unit 21 determines in step S1001
The image data is compressed by thinning it out, and the level is limited in step S1002, and the image data is output to the local decoding unit 2022 and the determination unit 2024 without performing correction at first. Local decoding unit 20
In step S1003, in step S1003, the correction data from the compression unit 21 (in the first place, as described above, the image data is obtained by simply limiting the level of the compressed data obtained by simply thinning out) is locally decoded.

That is, in step S1003, the compression unit 2
The correction data from 1 is locally decoded, for example, in the same manner as in the case of the local decoding unit 1022 shown in FIG. 17, whereby the predicted value of the original image is calculated. This predicted value is supplied to the error calculator 23.

In the error calculating section 23, Step S1004
, A prediction error is calculated in the same manner as in step S4 of FIG.
24. The determination unit 2024 determines whether the error
In step S1005, as in step S4 of FIG. 4, it is determined whether the error information from the error calculator 23 is equal to or smaller than a predetermined threshold ε. If it is determined in step S1005 that the error information is not equal to or smaller than the predetermined threshold ε, the process proceeds to step S1006, and the determination unit 2024 controls the compression unit 21 to thereby correct the compressed data whose level is limited. . Then, the compression unit 21 corrects the compressed data and waits for new correction data to be output, and then returns to step S1003.

On the other hand, if it is determined in step S1005 that the error information is equal to or smaller than the predetermined threshold ε, the determination unit 2024 proceeds to step S1007 to correct the error information obtained when the error information is equal to or smaller than the predetermined threshold ε. The data is output to the transmitter / recording device 16 (FIG. 2) as the optimal compressed data, and the process ends.

Therefore, here, the prediction coefficient for each class is not output.

Next, FIG. 22 shows an example of the configuration of the local decoding unit 2022 in FIG. In FIG. 1, FIG.
The same reference numerals are given to the portions corresponding to the case in FIG. That is, the local decoding unit 2022
The configuration is basically the same as that of the local decode unit 1022 in FIG.

However, in FIG. 17, the prediction coefficient ROM
81 is the prediction coefficient for each class stored therein,
In FIG. 22, the prediction coefficient ROM 81 stores the prediction coefficient for each class,
It is not supplied to the determination unit 24.

Next, the operation will be described with reference to the flowchart of FIG.

In steps S2021 to S2024, the local decoding unit 2022 performs the same processing as in steps S1021 to S1024 in FIG. Then, in step S2025, only the prediction value obtained in step S2024 is supplied to the error calculation unit 23 (the prediction coefficient is determined by the determination unit 2024).
Is returned to step S2021.
The same processing is repeated, for example, for each frame.

Next, FIG. 24 is a diagram showing the determination unit 2024 of FIG.
Is shown. In the figure, parts corresponding to those in FIG. 13 are denoted by the same reference numerals. That is, as described above, the local decoding unit 202
Since no prediction coefficient is supplied from No. 2, the judgment unit 2024
Has a prediction coefficient memory 6 for storing the prediction coefficient.
1 is not provided, except for that, basically,
The configuration is the same as that of the determination unit 24 in FIG.

Next, the operation will be described with reference to the flowchart of FIG.

In the judging section 2024, the step S2041 is executed.
In steps S41 to S45 in FIG.
Processing similar to that in the case of 45 is performed, whereby the compression unit 21 is controlled so that error information decreases.

Then, steps S2041 to S204
By repeating the process of step 5, the error information decreases. If it is determined in step S2042 that the current error information is smaller than the predetermined threshold ε, the process proceeds to step S2046, where the control circuit 65 The correction data of one frame stored in the data memory 62 is
Read as optimal compressed data, transmitter / recorder 16
To terminate the process.

After that, after the error information for the next frame is supplied, the same processing is repeated again.

Next, FIG. 26 shows an example of the configuration of the receiving apparatus 4 when the transmitting apparatus 1 is configured as shown in FIG. In the figure, the same reference numerals are given to portions corresponding to the case in FIG. That is, FIG.
The receiving device 4 has basically the same configuration as that in FIG. 15 except that a prediction coefficient ROM 78 is newly provided between the class classification circuit 75 and the prediction circuit 76.

When transmitting apparatus 1 is configured as shown in FIG. 20, as described above, encoded data output from receiver / reproducing apparatus 71 does not include a prediction coefficient for each class. Therefore, in the separating unit 72, only the correction data (optimized compressed data) is extracted from the encoded data, and supplied to the class classification blocking circuit 73 and the predicted value calculation blocking circuit 77.

Classification blocking circuit 73, class classification circuit 75, or prediction value calculation blocking circuit 77
Now, the class classification blocking circuit 41 in FIG.
The same processing as that of the class classification circuit 45 or the prediction value calculation blocking circuit 42 is performed, whereby the prediction value calculation block of 5 × 5 pixels is output from the prediction value calculation blocking circuit 77. Also, the class classification circuit 7
5 outputs class information. The prediction value calculation block is supplied to the prediction circuit 76, and the class information is represented by a prediction coefficient R
OM78.

In the prediction coefficient ROM 78, the same prediction coefficient as that of each class stored in the prediction coefficient ROM 81 of FIG. 22 is stored. A 26 × 9 prediction coefficient corresponding to the information is read and supplied to the prediction circuit 76.

The prediction circuit 76 calculates the 26 × 9 prediction coefficients from the prediction coefficient ROM 78 and the correction data constituting the prediction value calculation block of 5 × 5 pixels supplied from the prediction value calculation blocking circuit 77. Using the equation (1), a predicted value of 3 × 3 pixels of the original image is calculated, and an image of one frame including such predicted values is output as a decoded image.

When transmitting apparatus 1 is configured as shown in FIG. 20 and receiving apparatus 4 is configured as shown in FIG. 26, it is not necessary to transmit and receive 26 × 9 prediction coefficients for each class. Therefore, the transmission capacity or the recording capacity can be reduced accordingly.

Note that the prediction coefficient ROMs 78 and 81 do not store the prediction coefficient for each class, but can store the average value of the pixel values constituting the teacher block for each class. It is. In this case, when the class information is given, the pixel value corresponding to the class is output, and the local decoding units 1022 and 2022 of FIGS. It is not necessary to provide 82. Also, in the receiving device 4 of FIG. 26, similarly, the prediction value calculating block circuit 77 and the prediction circuit 7
6 does not have to be provided.

In the above case, the correction of the compressed data is repeatedly performed until the error information becomes equal to or less than the predetermined threshold value ε. However, the upper limit is set for the number of times of the correction of the compressed data. It is also possible. That is, for example,
For example, when transmitting images in real time,
The processing for one frame needs to be completed within a predetermined period, but the error information does not always converge within such a predetermined period. Therefore, by providing an upper limit to the number of corrections, if the error information does not converge below the threshold value ε within a predetermined period, the processing for that frame is terminated (correction data at that time is used as an encoding result. ), It is possible to start processing for the next frame.

In the above case, the compression section 21 simply thins out the image, that is, extracts the central pixel in a 3 × 3 pixel block and uses it as compressed data. In addition, for example, the unit 21 is caused to obtain an average value of nine pixels constituting the block, and the average value is used as the pixel value of the central pixel in the block to reduce the number of pixels (thinning out). This can be used as compressed data.

Next, FIG. 27 shows a fourth example of the transmitting apparatus 1 of FIG.
2 shows an example of a functional configuration.

The blocking circuit 111 receives image data to be encoded. The blocking circuit 111 classifies the image data into a predetermined class according to the nature of the image data. ADRC is divided into blocks for class classification around the pixel of interest.
(Adaptive Dynamic Range Coding) is supplied to the processing circuit 112 and the delay circuit 115.

The ADRC processing circuit 112 performs ADRC processing on the block (class classification block) from the blocking circuit 111, and supplies the resulting block composed of the ADRC code to the class classification circuit 113. It has been made like that.

Here, according to the ADRC process, the number of bits of the pixels constituting the class classification block is reduced.

That is, for example, for simplicity of description, consider a block composed of four pixels arranged on a straight line as shown in FIG. 28 (A). Are detected as the maximum value MAX and the minimum value MIN. Then, DR = MAX−MIN is set as a local dynamic range of the block, and based on the dynamic range DR, the pixel values of the pixels forming the block are requantized to K bits.

[0221] That is, from each pixel value in the block, subtracts the minimum value MIN, dividing the subtracted value by DR / 2 ^K.
Then, the code (A) corresponding to the resulting division value
DRC code). Specifically, for example, K
In the case where = 2, as shown in FIG. 28B, it is determined whether the divided value belongs to any range obtained by equally dividing the dynamic range DR by 4 (= 2 ² ).
The range of the lowest level, the range of the second lowest level,
In the case of belonging to the range of the third level from the bottom or the range of the top level, for example, 00B, 0
It is encoded into two bits such as 1B, 10B, or 11B (B represents a binary number). Then, on the decoding side, the ADRC codes 00B, 01B, 10
B or 11B is the center value L ₀₀ of the range of the lowest level obtained by dividing the dynamic range DR into four, the center value L ₀₁ of the range of the second level from the bottom, and the center value L _{01 of} the third level from the bottom. The center value L ₁₀ of the range or the center value L ₁₁ of the highest level range is converted, and the minimum value MIN is added to the value to perform decoding.

Here, such ADRC processing is called non-edge matching.

The details of the ADRC processing are disclosed in, for example, Japanese Patent Application Laid-Open No. 3-53778, filed by the present applicant earlier.

According to the ADRC processing as described above, the number of bits can be reduced by performing requantization with a smaller number of bits than the number of bits allocated to the pixels constituting the block.

Returning to FIG. 27, the classification circuit 113
Performing a class classification process of classifying the blocks from the ADRC processing circuit 112 into predetermined classes according to their properties;
The class to which the block belongs is supplied to the mapping coefficient memory 114 as class information.

Here, in a case where, for example, 8 bits are allocated to the image data to be encoded, the class classification block is, for example, 3 ×
When the classification processing is performed on such a classification block when it is composed of 9 pixels of 3,
(2 ⁸ ) It will be classified into a huge number of classes of ⁹ .

Therefore, as described above, the ADRC processing circuit 112 assigns A
The DRC processing is performed, whereby the number of bits of pixels constituting the class classification block is reduced, and the number of classes is also reduced. That is, in the ADRC processing circuit 112, for example, 1
Assuming that bit ADRC processing is performed, the number of classes is reduced from (2 ⁸ ) ⁹ to (2 ¹ ) ⁹ , that is, 512.

In the present embodiment, the class classification circuit 113 performs the class classification processing based on the ADRC code output from the ADRC processing circuit 112.
(Predictive coding), BTC (Block Truncation Codin)
g), VQ (vector quantization), DCT (discrete cosine transform), Hadamard transform and the like can be performed on the data.

Referring back to FIG. 27, the mapping coefficient memory 114 stores mapping coefficients for each class obtained by learning (mapping coefficient learning) described later, and the class information supplied from the class classification circuit 113. Is read out, and the arithmetic circuit 1
16.

The delay circuit 115 includes a blocking circuit 111
Is stored in the mapping coefficient memory 1 in a mapping coefficient corresponding to the class information of the block.
The data is delayed until the data is read from the memory 14, and is supplied to the arithmetic circuit 116.

The arithmetic circuit 116 performs a predetermined arithmetic operation using the pixel values of the pixels constituting the block supplied from the delay circuit 115 and the mapping coefficients supplied from the mapping coefficient memory 114 and corresponding to the class of the block. Is performed, encoded data obtained by encoding the image by thinning out (reducing) the number of pixels is calculated. That is, the arithmetic circuit 116 is the blocking circuit 1
The pixel value of each pixel constituting the block output by 11 is y
_1, y _2, with a., The mapping coefficient memory 114 outputs, k _1, k ₂ the mapping coefficients corresponding to the class of the block, when a., Of predetermined to them an argument Function value f (y ₁ , y ₂ ,..., K ₁ ,
k ₂ ,...) and the function value f (y ₁ , y ₂ ,.
.., K ₁ , k ₂ ,...) Are output as, for example, the pixel value of the central pixel among the pixels constituting the block (class classification block) output by the blocking circuit 111. ing.

Therefore, assuming that the number of pixels constituting the class classification block output from the blocking circuit 111 is N pixels, the arithmetic circuit 116 thins out the image data to 1 / N,
This is output as encoded data.

The encoded data output from the arithmetic circuit 116 is not obtained by a so-called simple thinning-out process in which one pixel at the center of a block composed of N pixels is selected and output. , As described above, the function value f (y defined by the N pixels constituting the block
_{1, y 2, ···, k} 1, k 2, it is a ...), the function value _{f (y 1, y 2,} ···, k 1, k 2, ···) is In other words, it can be considered that the pixel value of the pixel at the center of the block, which is obtained by a simple thinning process, is corrected based on the pixel values of the surrounding pixels. Therefore, the encoded data, which is data obtained as a result of the operation of the mapping coefficients and the pixels constituting the block, is hereinafter also referred to as correction data as appropriate.

In the arithmetic processing in the arithmetic circuit 116, the pixel value of each pixel constituting the class classification block output from the blocking circuit 111 is converted into a function value f (y ₁ ,
y ₂ ,..., k ₁ , k ₂ ,...). Therefore, the coefficients k ₁ , k ₂ ,... Used for such processing are called mapping coefficients.

The transmitting / recording device 118 is
The correction data supplied as encoded data from 6 is recorded on the recording medium 2 or transmitted via the transmission path 3.

Next, the operation will be described with reference to the flowchart of FIG.

For example, image data is supplied to the blocking circuit 111 in units of one frame (field). In step S61, the blocking circuit 111 converts the image of one frame into a class Blocked into blocks. That is, the blocking circuit 1
Reference numeral 11 denotes, for example, dividing the image data into 9 × 3 × 3 (horizontal × vertical) pixels around the pixel of interest as shown in FIG.
The signals are sequentially supplied to the processing circuit 112 and the delay circuit 115.

In this case, the class classification block is formed of a square block composed of 3 × 3 pixels. However, the shape of the class classification block does not need to be a square. For example, the shape can be a rectangle, a cross, or any other shape. Also,
The number of pixels constituting the class classification block is also 3 × 3 = 9.
It is not limited to pixels. In addition, the class classification block may be configured not with adjacent pixels but with separated pixels. However, the shape and the number of pixels need to match those at the time of learning (mapping coefficient learning) described later.

Upon receiving the class classification block from the blocking circuit 111, the ADRC processing circuit 112 performs, for example,
A 1-bit ADRC process is performed, thereby forming a block composed of pixels represented by 1 bit. ADR
The classification block subjected to the C processing is supplied to the classification circuit 113.

In the class classification circuit 113, step S6
In 3, the class classification block from the ADRC processing circuit 112 is classified, and the resulting class information is supplied to the mapping coefficient memory 114 as an address. Thereby, the mapping coefficient memory 11
From 4, the mapping coefficient corresponding to the class information supplied from the class classification circuit 113 is read and supplied to the arithmetic circuit 116.

On the other hand, in the delay circuit 115, the class classification block from the blocking circuit 111 is delayed, and after the mapping coefficient corresponding to the class information of the block is read from the mapping coefficient memory 114, the operation circuit 116. In step S64, the arithmetic circuit 116 uses the pixel value of each pixel constituting the class classification block from the delay circuit 115 and the mapping coefficient from the mapping coefficient memory 114 to obtain the above-described function value f (·) (this .. In parentheses of the function f represent a set of pixel values y ₁ , y ₂ ,... And mapping coefficients k ₁ , k ₂ ,. Correction data is calculated by correcting the pixel value of the central pixel (central pixel) constituting the application block. This correction data is supplied to the level limiting circuit 117 as encoded data obtained by encoding an image.

In level limiting circuit 117, step S6
At 5, for example, the two bits on the MSB side are extracted from the encoded data and supplied to the transmitter / recorder 118. That is, if the encoded data is composed of, for example, 8 bits, the lower 6 bits are deleted and the upper 2 bits are deleted.
Only the bits are provided to the transmitter / recorder 16. In the transmitter / recording device 118, in step S66,
The encoded data from the level limiting circuit 117 is recorded on the recording medium 2 or transmitted via the transmission path 3.

Then, the flow advances to a step S67 to determine whether or not the processing for the image data for one frame has been completed. If it is determined in step S67 that the process for one frame of image data has not been completed, the process returns to step S62, and the processes in step S62 and subsequent steps are repeated for the next class classification block. If it is determined in step S67 that the processing for one frame of image data has been completed, the process returns to step S61, and the next frame is targeted.
The processing from step S61 is repeated.

Next, FIG. 30 shows an example of the configuration of an image processing apparatus which performs learning (mapping coefficient learning) processing for calculating mapping coefficients stored in the mapping coefficient memory 114 of FIG.

In the memory 121, one or more frames of digital image data suitable for learning (hereinafter, appropriately referred to as learning images) are stored. The blocking circuit 122 includes:
Reads image data stored in the memory 121,
The same block as the class classification block output from the blocking circuit 111 in FIG. 27 is configured to be supplied to the ADRC processing circuit 123 and the arithmetic circuit 125.

The ADRC processing circuit 123 or the class classification circuit 124 performs the same processing as the ADRC processing circuit 112 or the class classification circuit 113 shown in FIG. 27, respectively. Therefore, the class classification circuit 124 outputs the class information of the block output by the blocking circuit 122. The class information is stored in the mapping coefficient memory 131.
Is supplied as an address.

The arithmetic circuit 125 includes a blocking circuit 122
The same operation as that in the arithmetic circuit 116 of FIG. 27 is performed using the pixels constituting the block supplied from the pixel and the mapping coefficient supplied from the mapping coefficient memory 131, and the correction data (function value f
(•)) is supplied to the level limiting circuit 126. After limiting the level of the pixel data, the level limiting circuit 126 outputs the pixel data to the local decoding unit 127.

Based on the correction data supplied from the level limiting circuit 126, the local decoding unit 127 calculates the prediction value of the original learning image (the prediction value of the pixel value of the pixel forming the block output by the blocking circuit 122). ) Is predicted (calculated) and supplied to the error calculator 128. The error calculation unit 128 is provided for the local decoding unit 12.
The pixel value (true value) of the learning image corresponding to the prediction value supplied from 7 is read from the memory 121, and the prediction error of the prediction value with respect to the pixel value of the learning image is calculated (detected).
Then, the prediction error is supplied to the determination unit 129 as error information.

The determining section 129 compares the error information from the error calculating section 128 with a predetermined threshold value ε1, and controls the mapping coefficient setting circuit 130 according to the comparison result. Mapping coefficient setting circuit 130
Sets (changes) a set of mapping coefficients of the same number as the number of classes obtained as a result of the classification in the classification circuit 124 according to the control of the determination unit 129, and supplies the same to the mapping coefficient memory 131. I have.

The mapping coefficient memory 131 temporarily stores the mapping coefficients supplied from the mapping coefficient setting circuit 130. Note that the mapping coefficient memory 131 has storage areas capable of storing mapping coefficients (sets of mapping coefficients) of the number of classes classified by the class classification circuit 124. In each storage area, When a new mapping coefficient is supplied from the mapping coefficient setting circuit 130, the new mapping coefficient is stored instead of the already stored mapping coefficient.

The mapping coefficient memory 131 reads out the mapping coefficient stored at the address corresponding to the class information supplied from the class classification circuit 124 and supplies it to the arithmetic circuit 126.

Next, the operation will be described with reference to the flowchart in FIG.

First, the mapping coefficient setting circuit 13
In step S 71, a set of initial values of mapping coefficients is set to the number of classes classified by the class classification circuit 124 in step S 71 and supplied to the mapping coefficient memory 131. In the mapping coefficient memory 131,
The mapping coefficient (initial value) from the mapping coefficient setting circuit 130 is stored at the address of the corresponding class.

Then, in step S72, the blocking circuit 122 converts all the learning images stored in the memory 121 into a 3 × 3 image centered on the pixel of interest in the same manner as in the blocking circuit 111 of FIG. Block into blocks of pixels. Further, the blocking circuit 12
2 reads the block from the memory 121 and
It is sequentially supplied to the DRC processing circuit 123 and the arithmetic circuit 125.

In the ADRC processing circuit 123, step S
At 73, the block from the blocking circuit 122 is subjected to 1-bit ADRC processing as in the case of the ADRC processing circuit 112 in FIG. 27, and is supplied to the class classification circuit 124.

In the classifying circuit 124, step S7
In 4, the class of the block supplied from the ADRC processing circuit 123 is determined, and the class information is supplied to the mapping coefficient memory 131 as an address.
Thereby, in step S75, the mapping coefficient is read from the address corresponding to the class information supplied from the class classification circuit 124 in the mapping coefficient memory 131, and supplied to the arithmetic circuit 125.

The operation circuit 125 includes a blocking circuit 122
When receiving a block from the mapping coefficient memory 131 and receiving a mapping coefficient corresponding to the class of the block from the mapping coefficient memory 131, in step S76, the mapping coefficient and the pixel of the pixel constituting the block supplied from the blocking circuit 122 are received. The above function value f (·) is calculated using the values. This calculation result is output as correction data obtained by correcting the pixel value of the central pixel of the block output from the blocking circuit 122 as the level limiting circuit 126
Supplied to Assuming that the correction bits are composed of, for example, 8 bits, in step S77, the upper two bits are selected by the level limiting circuit 126 from the correction data of 8 bits, whereby the number of bits is 2 bits. Is supplied to the local decoding unit 127.

That is, for example, assuming that a block of 3 × 3 pixels as shown by a square in FIG. 7 is output from the block forming circuit 122, the arithmetic circuit 125 The correction data obtained by correcting the pixel value of the pixel indicated by is obtained, the level is limited by the level limiting circuit 126, and is output to the local decoding unit 127.

Therefore, in the arithmetic circuit 126, the pixels constituting the learning image are thinned out to 1/9 and supplied to the local decoding section 127.

Returning to FIG. 31, the flow advances to step S78 to determine whether correction data has been obtained for all the learning images stored in the memory 121.
If it is determined in step S78 that the correction data for all the learning images has not been obtained yet, the process returns to step S73, and the processing in steps S73 to S78 is performed until the correction data for all the learning images is obtained. Repeat the process.

If it is determined in step S78 that the correction data for all the learning images has been obtained, that is, a thinned image obtained by thinning out all the learning images stored in the memory 121 to 1/9. Is obtained (however, this thinned-out image is simply a learning image,
/ 9, not pixel values obtained by calculation with mapping coefficients), step S7
The local decoding unit 127 locally decodes the thinned-out image to calculate the predicted value of the original learning image. This predicted value is supplied to the error calculator 128.

Here, an image composed of predicted values obtained by the local decoding section 127 (however, as described later, when the error information output from the error calculating section 128 becomes smaller than the threshold value ε1) Is the same as the decoded image obtained on the receiving device 4 (FIG. 1) side.

In step S80, the error calculating section 128 reads the learning image from the memory 121, and calculates the prediction error of the predicted value supplied from the local decoding section 127 for the learning image. That is, when the pixel value of the learning image is expressed as Y _ij and the predicted value output from the local decoding unit 127 is expressed as E [Y _ij ], the error calculation unit 128 calculates the error variance expressed by the following equation. (Error sum of squares) Q is calculated, and this is supplied to the determination unit 129 as error information.

Q = Σ (Y _ij -E [Y _ij ]) ^{2 In} the above equation, Σ represents the summation for all the pixels of the learning image.

Upon receiving the error information from the error calculating unit 128, the determining unit 129 compares the error information with a predetermined threshold ε1, and determines the magnitude relation in step S81. In step S81, the error information is equal to the threshold ε1
If it is determined that the above is true, that is, an image composed of predicted values obtained in the local decoding unit 127 is
If it is not recognized that the image is the same as the original learning image, the determination unit 129 outputs a control signal to the mapping coefficient setting circuit 130. In step S82, the mapping coefficient setting circuit 130 changes the mapping coefficient according to the control signal from the determination unit 129, and causes the mapping coefficient memory 131 to newly store the changed mapping coefficient.

Then, the process returns to step S73, and the processing from step S73 is repeated again using the changed mapping coefficient stored in mapping coefficient memory 131.

Here, the mapping coefficient in the mapping coefficient setting circuit 130 may be changed at random, or when the current error information is smaller than the previous error information, the same as the previous time. Change with the tendency of
When the current error information becomes larger than the previous error information, the error information may be changed in a reverse tendency.

Further, the change of the mapping coefficient can be performed for all classes, or can be performed for only some of the classes. In the case of changing only the mapping coefficients of some classes, for example, a class having a strong influence on the error information may be detected, and only the mapping coefficients of such classes may be changed. The class having a strong influence on the error information can be detected, for example, as follows. That is, first, processing is performed using the initial value of the mapping coefficient to obtain the error information. Then, the mapping coefficient is changed by the same amount for each class, and the resulting error information is compared with the error information obtained when the initial value is used, and the difference is equal to or more than a predetermined value. Class
What is necessary is just to detect as a class which has a strong influence on error information.

Also, the mapping coefficient is k1,
In the case where a plurality is set as one set such as k2,..., it is also possible to change only those having a strong influence on the error information.

Further, in the above case, the mapping coefficient is set for each class. However, the mapping coefficient may be set independently for each block, or may be set for each adjacent block. It is possible to set it with.

However, for example, when the mapping coefficients are independently set for each block, a plurality of sets of mapping coefficients may be obtained for a certain class (inversely, a plurality of sets of mapping coefficients may be obtained). , There may be a class for which no set of mapping coefficients can be obtained.) Since the mapping coefficients must be finally determined for each class, as described above, when a plurality of sets of mapping coefficients are obtained for a certain class,
For example, it is necessary to determine one set of mapping coefficients by performing some processing such as merging or selection on a plurality of sets of mapping coefficients.

On the other hand, when it is determined in step S81 that the error information is smaller than the threshold value ε1, that is, the image composed of the predicted values obtained in the local decoding unit 127 is the same as the original learning image. If accepted, the process ends.

At this point, the mapping coefficient memory 131
The mapping coefficient of FIG. 27 is determined as the optimal one to obtain the correction data that can restore the decoded image (prediction value) that is recognized as the same as the original image. It is set in the memory 114.

Therefore, by generating correction data using such a mapping coefficient, it becomes possible for the receiving device 4 (FIG. 1) to obtain an image substantially the same as the original image.

Here, in the embodiment of FIG. 30,
As described above, the image is divided into 9 pixels of 3 × 3 in the blocking circuit 122 and the ADRC processing circuit 123 performs 1-bit ADRC processing. The number of classes obtained is 512 (= (2 ¹ ) ⁹ ), so that 512 sets of mapping coefficients are obtained.

In FIG. 30, the block supplied to the classifying circuit 124 via the ADRC processing circuit 123 and the block supplied to the arithmetic circuit 125 need not necessarily be the same. This is the same for the block supplied to the class classification circuit 113 and the block supplied to the arithmetic circuit 116 in FIG. However, the blocks supplied to the classifying circuits 113 and 124 need to be the same, and the arithmetic circuit 1
The blocks supplied to 16 and 125 also need to be identical.

Next, FIG. 32 shows an example of the configuration of the local decoding section 127 of FIG.

The correction data from the level limiting circuit 126 is supplied to the class classification blocking circuit 141 and the predicted value calculation blocking circuit 142. The class classification blocking circuit 141 is configured to block the correction data into a class classification block centering on the target correction data, which is a unit for classifying the correction data into a predetermined class according to its properties. .

That is, as described above, in FIG. 7, the i-th correction data (compressed data) (or pixel) (or pixel) from the left (the portion indicated by a _{black circle in the} figure) is represented by _Xij. Then, the class classification blocking circuit 141
Four pixels X _{(i-1) j} , X _{i (j-1)} , X _{i (j + 1)} , X _{(i )} adjacent to the pixel of interest (correction data of interest) X _ij above, left, right, and below
_{-1) A} class classification block composed of a total of 5 pixels, including itself, is generated in _j . The classification block is supplied to the classification circuit 144.

The class classification block obtained by the class classification blocking circuit 141 shown in FIG. 32 is configured to determine the class of the block for which the prediction value is to be obtained. In order to determine the class of the block to be calculated, it differs from that generated by the blocking circuit 111 in FIG.

The prediction value calculation blocking circuit 142
Positive data is predicted from the original image (here, the learning image)
Focus on the target correction data, which is the unit for calculating the value
Block to predict value calculation block
Have been. That is, in the present embodiment, for example,
Correction data X_ijTo the original image (original image)
Pixel value Y of 3 × 3 9 pixels_ij(1), Y_ij(2),
Y_ij(3), Y_ij(4), Y_ij(5), Y_ij(6), Y
_ij(7), Y_ij(8), Y_ijThe predicted value of (9) is the pixel X
_ij5 × 5 25 pixels X centered on_{(i-2) (j-2)}, X
_{(i-2) (j-1)}, X_{(i-2 ) j}, X_{(i-2) (j + 1)}, X_{(i-2) (j + 2)},
X_{(i-1) (j-2)}, X_{(i-1) (j-1)}, X_{(i-1) j},
X_{(i-1 ) (j + 1)}, X_{(i-1) (j + 2)}, X_{i (j-2)}, X_{i (j-1)}, X
_ij, X_{i (j + 1)}, X_{i (j + 2)}, X_{(i +1) (j-2)},
X_{(i + 1) (j-1)}, X_{(i + 1) j}, X_{(i + 1) (j + 1)},
X_{(i + 1) (j + 2)}, X_{(i + 2 ) (j- 2)}, X_{(i + 2) (j-1)},
X_{(i + 2) j}, X_{(i + 2) (j + 1)}, X_{(i + 2) (j + 2)}Required from
The prediction value calculation blocking circuit 14
2 is such a square prediction composed of 25 pixels
A value calculation block is generated.

More specifically, for example, pixels Y ₃₃ (1) to Y ₃₃ in the original image, which are surrounded by a square in FIG.
_{33 To} calculate the predicted value of (9), 25 pixels (correction data) X ₁₁ , X ₁₂ , X ₁₃ , X ₁₄ , X ₁₅ , X ₂₁ ,
_{X 22, X 23, X 24} , X 25, X 31, X 32, X 33, X 34, X
₃₅ , _X41 , _X42 , _X43 , _X44 , _X45 , _X51 , _X52 ,
X ₅₃ , X ₅₄ , and X _{55 form} a predicted value calculation block.

The predicted value calculation block obtained by the predicted value calculation blocking circuit 142 is supplied to the prediction circuit 146.

The prediction value calculation block is also
As in the case of the class classification block, the number and shape of the pixels are not limited to those described above. However, in the local decoding unit 127, it is desirable that the number of pixels forming the prediction value calculation block be larger than the number of pixels forming the class classification block.

The classifying circuit 144 classifies the blocks from the classifying blocking circuit 141 into classes and stores the class information as a result of the classification in the prediction coefficient ROM 14.
5. Prediction coefficient ROM14
5 stores the prediction coefficient for each class, and upon receiving the class information from the class classification circuit 144, reads out the prediction coefficient stored at the address corresponding to the class information and supplies it to the prediction circuit 146. It has been done. The prediction coefficients stored in the prediction coefficient ROM 145 are obtained by the prediction coefficient learning described with reference to FIG.

The prediction circuit 146 calculates the prediction value of the original image (learning image) using the prediction value calculation block from the prediction value calculation blocking circuit 142 and the prediction coefficient from the prediction coefficient ROM 145. (Forecast).

Next, the operation will be described with reference to the flowchart in FIG.

In local decoding section 127, first, in step S91, level limiting circuit 1
The correction data from 26 are sequentially received and blocked. That is, the correction data is divided into 5-pixel class classification blocks centering on the target correction data in the class classification blocking circuit 141, and is supplied to the class classification circuit 144. In the circuit 142, the correction data is divided into 5 × 5 pixel prediction value calculation blocks centered on the target correction data, and supplied to the prediction circuit 146.

The classifying block circuit 141
The prediction value calculation block generation circuit 142 generates a corresponding class classification block and a prediction value calculation block. That is, in the classification block forming circuit 141, for example, when a classification block of 5 pixels centered on the correction data X ₃₃ of Figure 7 is generated, the prediction value in the calculation block, also the correction data X ₃₃ A prediction value calculation block of 5 × 5 pixels at the center is generated.

The classifying circuit 144 determines in step S92
, A class classification process is performed on the class classification block. That is, the state of the level distribution of the correction data forming the class classification block is detected, and the class to which the class classification block belongs (the class of the target correction data forming the class classification block) is determined. The determination result of this class is represented by a prediction coefficient RO as class information.
M145.

In this case, the class classification process is performed on the class classification block composed of five pixels in which one pixel is represented by 2 bits. Therefore, each class classification block has 512 (= (2 ² ) It will be classified into one of the classes of ⁵ ).

Then, in step S93, the prediction coefficient is read from the address corresponding to the class information from the class classification circuit 144 in the prediction coefficient ROM 145. In step S94, the prediction circuit 146 stores the prediction coefficient The pixel values of the original image are calculated according to, for example, the following linear linear expression corresponding to Expression (1) using the 25 pixel values constituting the predicted value calculation block from the value calculation blocking circuit 142. A predicted value E [y] of the value y is calculated.

E [y] = w ₁ x ₁ + w ₂ x ₂ +... + W ₂₅ x ₂₅ + w ₀ where w ₁ , w ₂ ,..., W ₀ represent prediction coefficients, and x ₁ ,
x _2, · · · represents pixel values of the pixels constituting the predicted values calculation block (correction data). Also, w ₀ is an offset coefficient that is added to express E [y] with 8 bits because data x _{i of} 8 bits is originally converted into data of 2 bits. Treated as prediction coefficients, similarly to the coefficients w ₁ , w ₂ ,... Multiplied by the correction data.

Here, in the embodiment of FIG. 32,
As described above, the prediction value calculation block 25
A prediction value of 9 pixels is calculated from the pixels.

That is, for example, for the class classification consisting of 3 × 3 correction data X _{22 to} X ₂₄ , X _{32 to} X ₃₄ , and X _{42 to} X ₄₄ centered on the correction data X ₃₃ shown in FIG. The class information C of the block is stored in the classifying circuit 1
Is output from 44, also as the predicted value calculation block, correction data X ₁₁ to X ₁₅ of 5 × 5 pixels around the correction data X _33, X ₂₁ to X _25, X ₃₁ to, X _35, X ₄₁
To X ₄₅ and X _{51 to} X ₅₅ are output from the predicted value calculation blocking circuit 142.

Further, it is assumed that w ₀ (k) to w ₂₅ (k) are stored in the prediction coefficient ROM 145 at the address corresponding to the class information C as a set of prediction coefficients.

In this case, the correction data X ₃₃ is
Predicted values E [Y ₃₃ (1)] to E [Y ₃₃ () of pixel values Y ₃₃ (1) to Y ₃₃ (9) of 3 × 3 pixels (a portion surrounded by a square in FIG. 7) in the original image 9)] is calculated according to the following equation.

E [Y ₃₃ (k)] = w ₁ (k) X ₁₁ + w
_{2 (k) X 12 + w} 3 (k) X 13 + w 4 (k) X 14 + w
₅ (k) X ₁₅ + w ₆ (k) X ₂₁ + w ₇ (k) X ₂₂ + w
_{8 (k) X 23 + w} 9 (k) X 24 + w 10 (k) X 25 + w
₁₁ (k) X ₃₁ + w ₁₂ (k) X ₃₂ + w ₁₃ (k) X ₃₃ + w ₁₄
(K) X ₃₄ + w ₁₅ (k) X ₃₅ + w ₁₆ (k) X ₄₁ + w
₁₇ (k) X ₄₂ + w ₁₈ (k) X ₄₃ + w ₁₉ (k) X ₄₄ + w ₂₀
(K) X ₄₅ + w ₂₁ (k) X ₅₁ + w ₂₂ (k) X ₅₂ + w
₂₃ (k) X ₅₃ + w ₂₄ (k) X ₅₄ + w ₂₅ (k) X ₅₅ + w ₀
(K)

In step S94, a prediction value is obtained in units of nine as described above.
When the predicted value for the frame is obtained, the predicted value is supplied to the error calculator 128 in step S95. Then, the process returns to step S91, and thereafter, returns to step S91.
The processing from 91 to S95 is repeated.

The image processing apparatus for performing learning (prediction coefficient learning) for obtaining the prediction coefficients stored in the prediction coefficient ROM 145 of FIG. 32 has the same configuration as that shown in FIG. 19 as described above. Becomes Therefore, the description is omitted.

Next, FIG. 34 shows another example of the configuration of an image processing apparatus for performing a learning (mapping coefficient learning) process for calculating the mapping coefficients stored in the mapping coefficient memory 114 of FIG. .

According to the image processing apparatus shown in FIG. 30, the function f may be expressed by a non-linear equation or a quadratic or higher equation in addition to the case where the function f is expressed by a linear linear equation. Although the optimum prediction coefficient can be obtained, the image processing apparatus in FIG. 34 can obtain the optimum prediction coefficient only when the function f is represented by a linear linear expression.

That is, in the image processing apparatus of FIG. 34, in FIG. 27, the pixel values of the pixels constituting the 3 × 3 pixel block output by the blocking circuit 111 are represented by y ₁ , y ₂ ,.
, Y ₉ and the mapping coefficient memory 114
K _1, k ₂ but the mapping coefficients outputted, ..., in a case where a k _9, the arithmetic circuit 116, the function value f (y _1, y ₂ according to the following equation, ..., k _1, k ₂ ,...) Can be used when calculating correction data.

F (·) = k ₁ y ₁ + k ₂ y ₂ +... + K ₉ y ₉

A learning image suitable for learning is supplied to the optimum correction data calculating section 160, for example, in units of one frame. It should be noted that, for example, 8 bits are assigned to the pixels constituting the learning image.
The optimum correction data calculation section 160 includes a compression section 170, a level control section 171, a correction section 172, and a local decoding section 17.
3. An image which is composed of an error calculation unit 174 and a determination unit 175 and which is an image compressed by reducing the number of pixels from a learning image input thereto and which is optimal for predicting an original image Are calculated (hereinafter, appropriately referred to as optimum correction data) and supplied to the latch circuit 176.

That is, the learning image supplied to the optimum correction data calculating section 160 is compressed by the compressing section 170 and the error calculating section 1.
74. Compression unit 170
Is that the learning image is simply thinned out at the same rate as the rate at which the arithmetic circuit 116 in FIG. 27 thins out the pixels. That is, in this embodiment, the learning image is simply thinned out to 1/9 (3 × When nine pixels of 3 are taken as one block, only the pixel at the center of the block is extracted), whereby the learning image is compressed and supplied to the level limiting unit 171. The level limiting section 171 limits the level of 8-bit pixel data and outputs the same to the correcting section 172, similarly to the level limiting circuit 117 of FIG.

The correcting section 172 selects the upper 2 bits from the 8-bit compressed data which is supplied from the level limiting section 171 and which has been compressed in the spatial direction by performing simple thinning. The data whose level is limited to bits (hereinafter, simply referred to as compressed data as appropriate) is corrected under the control of the determination unit 175. Data obtained as a result of the correction by the correction unit 172 (this data is also obtained by correcting the pixel value of the central pixel of the 3 × 3 pixel block, similarly to the output of the arithmetic circuit 116 in FIG. 27. , Correction data) are supplied to the local decoding unit 173.

The local decoding section 173 predicts an original image (learning image) based on the correction data from the correction section 172 in the same manner as in the case of the local decoding section 127 in FIG. , Error calculating section 174
To be supplied.

The error calculator 174 inputs the data to the error calculator 174 in the same manner as in the error calculator 128 of FIG.
The prediction error of the prediction value from the local decoding unit 173 with respect to the original image data is calculated.
This prediction error is supplied to the determination unit 175 as error information.

The determination unit 175 converts the correction data output by the correction unit 172 based on the error information from the error calculation unit 174 into the compression result of the original image in the same manner as in the determination unit 129 of FIG. Is determined to be appropriate. When the determination unit 175 determines that it is not appropriate to use the correction data output by the correction unit 172 as the compression result of the original image, the correction unit 17
2 is further controlled, the compressed data is corrected, and new correction data obtained as a result is output. If the determining unit 175 determines that it is appropriate to use the correction data output from the correcting unit 172 as the compression result of the original image, the determining unit 175 determines that the correction data supplied from the correcting unit 172 is optimally corrected. As data, the latch circuit 17
6.

The latch circuit 176 has a built-in memory 176A, and stores the optimum correction data supplied from the correction section 172 in the memory 176A. Further, the latch circuit 176 stores the block correction circuit 17 out of the optimum correction data stored in the memory 176A.
The memory corresponding to the central pixel of the block read from the memory 177A of the seventh memory is read and supplied to the memory 180. When one frame of correction data is stored in the memory 176A, the latch circuit 176 outputs a control signal indicating this to the blocking circuit 177.

As in the case of the optimum correction data calculation section 160, the learning image is supplied to the blocking circuit 177 in units of one frame. Blocking circuit 17
7 has a built-in memory 177A.
The learning image supplied thereto is stored in 7A. Further, upon receiving the control signal from the latch circuit 176, the blocking circuit 177 receives the control signal from the memory 177.
The learning image stored in A is divided into blocks composed of 3 × 3 pixels, as in the case of the blocking circuit 111 in FIG. 27, and the blocks are sequentially read out.
The data is supplied to the DRC processing circuit 178 and the memory 180.

When reading a block from the built-in memory 177A, the blocking circuit 177 sends a control signal indicating the position of the block to the latch circuit 176.
To be supplied. The latch circuit 176 recognizes a 3 × 3 pixel block read from the memory 177A based on the control signal, and reads the optimal correction data corresponding to the center pixel of the block from the memory 176A as described above. It has been made like that. That is, a block of a certain 3 × 3 pixel and the optimum correction data corresponding to the block are simultaneously supplied to the memory 180.

The ADRC processing circuit 178 or the class classification circuit 179 has the same configuration as the ADRC processing circuit 112 or the class classification circuit 113 of FIG. 27, respectively. Then, the class information about the block from the blocking circuit 177 output from the class classification circuit 179 is supplied to the memory 180 as an address.

The memory 180 stores the optimum correction data supplied from the latch circuit 176 and the block supplied from the blocking circuit 177 in association with the address corresponding to the class information supplied from the class classification circuit 179. It has been made to be. The memory 180 has 1
A plurality of pieces of information can be stored in one address, whereby a plurality of sets of optimal correction data and blocks corresponding to certain class information can be stored.

The arithmetic circuit 181 stores nine pixels y ₁ , y ₂ ,..., Y ₉ constituting a 3 × 3 block of the learning image stored in the memory 180 in association with the block. By reading the optimal correction data y ′ and applying the least squares method to them, mapping coefficients k _{1 to} k ₉ are obtained for each class and supplied to the memory 182. The memory 182 stores the mapping coefficients k _{1 to} k ₉ for each class supplied from the arithmetic circuit 181 at addresses corresponding to the classes.

Next, the operation will be described with reference to the flowchart in FIG.

When the learning image is input, the learning image is stored in the memory 177A of the blocking circuit 177 and is supplied to the optimum correction data calculation section 160. Upon receiving the learning image, the optimum correction data calculation unit 160 calculates the optimum correction data for the learning image in step S101.

The processing in step S101 is the same as the processing in the flowchart in FIG. That is, in step S101, as shown in the flowchart of FIG.
First, in step S111, the compression unit 170 generates compressed data by thinning out the learning image to 1/9. This data is limited by the level limiting unit 171 in step S112, and then is first corrected without being corrected by the local decoding unit 1 via the correcting unit 172.
73 is output. In step S113, the local decoding unit 173 predicts the original image based on the correction data from the correction unit 172 (initially, as described above, the image data is simply thinned out and the level-limited compressed data itself). A value is calculated (local decoding is performed). This predicted value is supplied to the error calculator 174.

When receiving the predicted value of the original image from the local decoding unit 173, the error calculating unit 174 calculates the prediction error of the predicted value of the original image data from the local decoding unit 173 in step S114. The error information is supplied to the determination unit 175. Judgment unit 175
Upon receiving the error information from the error calculation unit 174, in step S115, based on the error information, determines whether the correction data output by the correction unit 172 is appropriate as the compression result of the original image.

That is, in step S115, it is determined whether or not the error information is equal to or less than a predetermined threshold ε.
If it is determined in step S115 that the error information is not less than or equal to the predetermined threshold ε, it is recognized that it is not appropriate to use the correction data output by the correction unit 172 as the compression result of the original image, and the process proceeds to step S116. Judgment unit 175
Controls the correcting unit 172, and thereby corrects the compressed data output from the level limiting unit 171. Under the control of the determination unit 175, the correction unit 172 corrects the compressed data by changing the correction amount (correction value △), and outputs the resulting correction data to the local decoding unit 173. Then, the process returns to step S113, and thereafter, the same processing is repeated.

The correction of the compressed data can be performed, for example, in the same manner as in the case of FIG.

On the other hand, if it is determined in step S115 that the error information is equal to or smaller than the predetermined threshold value ε, it is recognized that it is appropriate to use the correction data output by the correction unit 172 as the compression result of the original image. Then, the determination unit 175 causes the correction unit 172 to output the correction data obtained when error information equal to or smaller than the predetermined threshold value ε is obtained as the optimum correction data to the latch circuit 176 and store it in the built-in memory 176A. And return.

As described above, the correction data obtained by correcting the compressed data when the error information becomes equal to or less than the predetermined threshold value ε is stored in the memory 176A as the optimum correction data. Since the optimum correction data sets the error information to be equal to or less than a predetermined threshold value ε, the prediction value is calculated using the error information to obtain the original image (original image).
And the same image can be obtained.

Returning to FIG. 35, when one frame of optimal correction data is stored in the memory 176A, the latch circuit 176 outputs a control signal to the blocking circuit 177. Upon receiving the control signal from the latch circuit 176, the blocking circuit 177 divides the learning image stored in the memory 177A into blocks each including 3 × 3 pixels in Step S102. Then, the blocking circuit 177 reads the blocks of the learning image stored in the memory 177A and supplies the blocks to the ADRC processing circuit 178 and the memory 180.

At the same time, the blocking circuit 177
When reading a block from the memory 177A, a control signal indicating the position of the block is supplied to the latch circuit 176, and the latch circuit 176 responds to the control signal by:
The block of 3 × 3 pixels read from the memory 177A is recognized, and the optimum correction data corresponding to the central pixel of the block is read and supplied to the memory 180.

Then, the flow advances to step S103 to execute ADR
In the C processing circuit 178, the block from the blocking circuit 177 is subjected to ADRC processing, and further, in the class classification circuit 179, the block is classified.
This classification result is stored in the memory 180 as an address.
Supplied to

In the memory 180, in step S104, at the address corresponding to the class information supplied from the class classification circuit 179, the optimal correction data supplied from the latch circuit 176 and the block supplied from the blocking circuit 177 (learning). ) Are stored in association with each other.

The flow advances to step S105 to determine whether the memory 180 has stored the blocks for one frame and the optimum correction data. Step S10
In 5, when it is determined that the block for one frame and the optimal correction data are not yet stored in the memory 180, the next block is read from the blocking circuit 177 and the corresponding block is read from the latch circuit 176. The optimal correction data to be read out is read out, and the process returns to step S103, and thereafter, the processes after step S103 are repeated.

If it is determined in step S105 that the blocks for one frame and the optimum correction data have been stored in the memory 180, the flow advances to step S106 to determine whether the processing has been completed for all the learning images. You. If it is determined in step S106 that the processing for all the learning images has not been completed, the process returns to step S101, and the processing from step S101 is repeated for the next learning image.

On the other hand, if it is determined in step S106 that the processing for all the learning images has been completed, the process proceeds to step S107, where the arithmetic circuit 181 compares the optimal correction data and the blocks stored in the memory 180 with each other.
The data is read out for each class, and a normal equation as shown in Expression (7) is established by these. Further, the arithmetic circuit 181
Calculates the mapping coefficient for each class that minimizes the error by solving the normal equation in step S108. This mapping coefficient is calculated in step S10.
At 9, the data is supplied to and stored in the memory 182, and the processing is terminated.

When the function f is represented by a linear linear expression, the mapping coefficient stored in the memory 182 as described above is stored in the mapping coefficient memory 114 shown in FIG.
, And can be used to encode an image.

Note that, depending on the class, there may be cases where it is not possible to obtain as many normal equations as can obtain the mapping coefficients. In such a case, the arithmetic circuit 1 in FIG.
At 16, 3 output from the blocking circuit 111 is output.
For example, mapping coefficients for outputting, for example, an average value of nine pixels constituting a block of × 3 pixels, that is, k _{1 to} k ₉ = 1/9 are set as default values.

Next, FIG. 37 shows an example of the configuration of the receiving device 4 corresponding to the transmitting device 1 of FIG.

In the receiver / reproducing device 191, the encoded data recorded on the recording medium 2 is reproduced, or the encoded data transmitted via the transmission path 3 is received and supplied to the decoding unit 192. You.

The decoding unit 192 is composed of the classifying blocking circuits 193 to 198 corresponding to the classifying blocking circuits 141 to 146 in the local decoding unit 127 shown in FIG. 32, respectively. Therefore, in the decoding unit 192, FIG.
As in the case of the local decoding unit 127, a prediction value is obtained from the correction data, and an image composed of the prediction value is output as a decoded image.

That is, the correction data as the encoded data output from the receiver / reproducing device 191 is sequentially supplied to the classifying blocking circuit 193 or the predicted value calculating blocking circuit 194, and the classifying block shown in FIG. Blocking Circuit 141 or Predicted Value Calculation Blocking Circuit 14
In the same manner as in the case of 2, the blocks are divided into a class classification block or a predicted value calculation block. The classifying block is a classifying circuit 1
At 96, the prediction value calculation block is supplied to the prediction circuit 198, respectively.

The classifying circuit 196 performs a classifying process on the classifying block from the classifying blocking circuit 193, and outputs the class information to the prediction coefficient R
Output to OM197.

The prediction coefficient ROM 197 stores, for example, prediction coefficients for each class obtained by the image processing apparatus shown in FIG. 19, like the prediction coefficient ROM 145 shown in FIG. Upon receiving, read the prediction coefficient corresponding to the class information,
This is supplied to the prediction circuit 198. The prediction circuit 198 uses the prediction coefficient from the prediction coefficient ROM 197 and the correction data forming the prediction value calculation block from the prediction value calculation blocking circuit 194 to obtain a linear linear expression corresponding to the expression (1). Therefore, the predicted value of the original image is calculated, and thereby the original image is decoded.

As described above, the correction data sets the error information to be equal to or less than the predetermined threshold value. Therefore, the receiving device 4 can obtain a decoded image substantially the same as the original image.

In the case where the mapping coefficients are used, decoding is performed on the receiving side by performing normal interpolation using a device that decodes a thinned image by interpolation instead of the receiving device 4 as shown in FIG. Images can be obtained. However, the decoded image obtained in this case has deteriorated image quality (resolution).

The image processing apparatus to which the present invention has been applied has been described above.
This is particularly effective when encoding a television signal of a standard system such as the NTSC system and encoding a so-called high-vision television signal having a large data amount.

In the present embodiment, the block is formed for one frame image. However, the block collects pixels at the same position in, for example, a plurality of frames continuous in time series. It is also possible to configure it.

In the present embodiment, the sum of squares of the error is used as the error information.
It is possible to use the sum or the like of the raised values. Which one to use as error information is determined, for example, by
It is possible to determine based on the convergence and the like.

Further, for example, in the embodiment shown in FIG. 10, a normal equation is set for each frame, and a prediction coefficient for each class is obtained. It is also possible to establish a normal equation in units of one field or a plurality of frames. The same applies to other processes.

The present invention can also be implemented by hardware or by reading the application program from a recording medium such as a hard disk in which the application program for performing the above-described processing is recorded, and causing the computer to execute the program. Is feasible.

Further, in the present embodiment, the pixel value level is limited to the upper two bits, but the pixel value level may be limited to, for example, the upper three bits or more. It is.

[0348]

According to the image encoding apparatus of the first aspect and the image encoding method of the eleventh aspect, the level of the compressed data obtained by compressing the original image is limited. The limited compressed data is corrected, and corrected data is output. Further, an original image is predicted based on the correction data, the predicted value is output, and a prediction error of the predicted value with respect to the original image is calculated.
Then, the appropriateness of the correction data is determined based on the prediction error, and based on the result of the determination, the appropriate correction data is output as the encoding result of the original image. Therefore, it is possible to efficiently predict a high-quality image with a small amount of data.

According to the image decoding apparatus described in claim 12, the image decoding method described in claim 18, the transmission method described in claim 19, and the recording medium described in claim 20, encoded data Compresses the original image by reducing the number of pixels, limits the level of compressed data obtained by compressing the original image, corrects the level-limited compressed data, and outputs corrected data. Predicting an original image based on the correction data, outputting a predicted value thereof, calculating a prediction error of the predicted value with respect to the original image, and determining adequacy of the correction data based on the prediction error. Is repeated until the correction data becomes appropriate, and the corrected correction data is obtained. Therefore, although the data amount of the encoded data is small, a high-quality decoded image can be obtained from the encoded data having such a small data amount.

[0350] According to the image coding apparatus of the present invention and the image coding method of the present invention, pixels constituting an image are classified into a predetermined class according to their properties. The mapping coefficient corresponding to the class of the pixel of interest in the image is read from the mapping coefficient storage means storing the predetermined mapping coefficient. Then, by performing a predetermined operation using the mapping coefficient and the target pixel, correction data obtained by correcting the target pixel is calculated, and by limiting the level of the correction data, a code obtained by coding the image is obtained. Generated data. Therefore, it is possible to efficiently predict a high-quality image with a small amount of data.

According to the image decoding apparatus of the present invention, the image decoding method of the present invention, the transmission method of the present invention, and the recording medium of the present invention, Classifies the pixels constituting the image into predetermined classes according to their properties, and stores, for each class, a predetermined mapping coefficient from a mapping coefficient storage unit, which stores the specified attention in the image. By reading a mapping coefficient corresponding to the pixel class and performing a predetermined operation using the mapping coefficient and the pixel of interest,
The correction data obtained by correcting the pixel of interest is obtained by limiting the level of the correction data. Therefore, although the data amount of the encoded data is small, a high-quality decoded image can be obtained from the encoded data having such a small data amount.

[Brief description of the drawings]

FIG. 1 is a block diagram illustrating a configuration of an embodiment of an image processing apparatus to which the present invention has been applied.

FIG. 2 is a block diagram illustrating a configuration example of a transmission device 1 of FIG.

FIG. 3 is a block diagram illustrating a first functional configuration example of a transmission device 1 of FIG. 2;

FIG. 4 is a flowchart for explaining an operation of the transmission device 1 of FIG. 3;

FIG. 5 is a block diagram illustrating a configuration example of a compression unit 21 in FIG. 3;

FIG. 6 is a flowchart for explaining the operation of the compression unit 21 of FIG.

FIG. 7 is a diagram for explaining processing of the thinning circuit 30 of FIG. 5;

FIG. 8 is a block diagram illustrating a configuration example of a local decoding unit 22 in FIG. 3;

FIG. 9 is a diagram illustrating a class classification process.

FIG. 10 is a flowchart for explaining the operation of a local decoding unit 22 in FIG. 8;

11 is a block diagram illustrating a configuration example of an error calculation unit 23 in FIG.

FIG. 12 is a flowchart for explaining the operation of the error calculator 23 in FIG. 11;

13 is a block diagram illustrating a configuration example of a determination unit 24 in FIG.

FIG. 14 is a flowchart for explaining the operation of the determination unit 24 in FIG.

FIG. 15 is a block diagram showing a first configuration example of the receiving device 4 of FIG. 1;

FIG. 16 is a block diagram illustrating a second functional configuration example of the transmission device 1 in FIG. 2;

17 is a block diagram illustrating a configuration example of a local decoding unit 1022 in FIG.

FIG. 18 is a flowchart for explaining the operation of the local decoding unit 1022 in FIG. 17;

19 is a block diagram illustrating a configuration of an embodiment of an image processing apparatus that calculates a prediction coefficient stored in a prediction coefficient ROM 81 of FIG.

20 is a block diagram illustrating a third functional configuration example of the transmission device 1 in FIG.

FIG. 21 is a flowchart for explaining the operation of the transmitting apparatus 1 of FIG.

22 is a block diagram illustrating a configuration example of a local decoding unit 2022 in FIG.

FIG. 23 is a flowchart illustrating the operation of a local decoding unit 2022 in FIG. 22.

24 is a block diagram illustrating a configuration example of a determination unit 2024 in FIG.

FIG. 25 is a flowchart for explaining the operation of the determination unit 2024 of FIG.

FIG. 26 is a block diagram illustrating a second configuration example of the receiving device 4 of FIG.

FIG. 27 is a block diagram illustrating a fourth functional configuration example of the transmission device 1 of FIG. 2;

FIG. 28 is a diagram for explaining an ADRC process.

FIG. 29 is a flowchart for explaining the operation of the transmission device 1 of FIG. 27.

FIG. 30 is a block diagram illustrating a configuration of a first embodiment of an image processing apparatus that performs learning for obtaining mapping coefficients.

FIG. 31 is a flowchart illustrating an operation of the image processing apparatus of FIG. 30;

FIG. 32 is a block diagram illustrating a configuration example of a local decoding unit 127 in FIG. 30.

FIG. 33 is a flowchart illustrating the processing of local decoding section 127 in FIG. 32;

FIG. 34 is a block diagram illustrating a configuration of a second embodiment of an image processing apparatus that performs learning for obtaining a mapping coefficient.

FIG. 35 is a flowchart illustrating the operation of the image processing apparatus of FIG. 34;

FIG. 36 is a flowchart for explaining the processing of step S101 in FIG. 35 in more detail;

FIG. 37 is a block diagram illustrating a third configuration example of the reception device 4 in FIG. 1.

[Explanation of symbols]

1 transmission device, 2 recording medium, 3 transmission line, 4
Receiver, 11 I / F, 12 ROM, 13
RAM, 14 CPU, 15 external storage device, 1
Reference Signs List 6 transmitter / recording device, 21 compression unit, 22 local decoding unit, 23 error calculation unit, 24 determination unit, 25 multiplexing unit, 30 thinning circuit, 31
Level limiting circuit, 32 correction circuit, 33 correction value R
OM, 41 Classification Blocking Circuit, 42
Predicted value calculation blocking circuit, 43 class classification adaptive processing circuit, 45 class classification circuit, 46 adaptive processing circuit, 51 blocking circuit, 52 square error calculation circuit, 53, 54 arithmetic unit, 55 integrator, 56
Memory, 61 prediction coefficient memory, 62 correction data memory, 63 error information memory, 64 comparison circuit, 65 control circuit, 71 receiver / reproducer, 7
2 Separator, 73 block circuit for class classification,
75 Classification circuit, 76 Prediction circuit, 76A
Memory, 77, a block circuit for calculating predicted values, 78,
81 prediction coefficient ROM, 82 prediction circuit, 91 learning block circuit, 92 teacher block circuit,
94 class classification circuit, 95 switch, 96 learning data memory, 97 counter, 98 teacher data memory, 99 arithmetic circuit, 100 memory, 11
1 block circuit, 112 ADRC processing circuit,
113 class classification circuit, 114 mapping coefficient memory, 115 delay circuit, 116 arithmetic circuit, 1
17 level limiting circuit, 118 transmitter / recorder,
121 memory, 122 blocking circuit, 12
3 ADRC processing circuit, 124 class classification circuit,
125 arithmetic circuit, 126 level limiting circuit, 12
7 local decoding unit, 128 error calculating unit, 1
29 determination unit, 130 mapping coefficient setting circuit,
131 mapping coefficient memory, 141 class classification blocking circuit, 142 predicted value calculation blocking circuit, 144 class classification circuit, 145 prediction coefficient ROM, 146 prediction circuit, 160 optimal correction data calculation section, 170 compression section, 171 level limit Unit, 172 correction unit, 173 local decoding unit, 174 error calculation unit, 175 determination unit, 17
6 latch circuit, 176A memory, 177 blocking circuit, 177A memory, 178 ADRC processing circuit, 179 class classification circuit, 180 memory,
181 arithmetic circuit, 182 memory, 191 receiver / reproducing device, 192 decoding unit, 193 block circuit for class classification, 194 block circuit for prediction value calculation, 196 class classification circuit, 197 prediction coefficient ROM, 198 prediction circuit, 1022 , 202
2 Local decoding unit, 2024 judgment unit

Claims (39)

[Claims] 1. An image encoding apparatus for encoding an image, comprising: compression means for compressing an original image by reducing the number of pixels thereof; and a level of compressed data obtained by compressing the original image. Limiting means for correcting the compressed data of which level is limited, correcting means for outputting corrected data, and predicting means for predicting the original image based on the corrected data and outputting a predicted value thereof. Calculating means for calculating a prediction error of the predicted value with respect to the original image; determining means for determining, based on the prediction error, appropriateness of the correction data output by the correcting means; Output means for outputting the correction data as an encoding result of the original image in accordance with the determination result. 2. The predicting unit includes: a classifying unit that classifies the correction data into a predetermined class according to a property thereof; and a prediction value calculating unit that obtains the predicted value corresponding to the class. The image encoding device according to claim 1, wherein: 3. The prediction means comprises: a prediction coefficient calculation means for obtaining a prediction coefficient for calculating the prediction value by a linear combination with the correction data; and a prediction for obtaining the prediction value from the prediction coefficient and the correction data. 2. The apparatus according to claim 1, further comprising a value calculating unit.
An image encoding device according to claim 1. 4. The predicting unit: a classifying unit that classifies the correction data into a predetermined class according to a property thereof; and a prediction coefficient for calculating the prediction value by a linear combination with the correction data, A prediction coefficient calculation unit for obtaining the prediction value from the prediction coefficient obtained for the class of the correction data and the correction data; and a prediction value calculation unit for obtaining the prediction value from the correction data. Item 2. The image encoding device according to Item 1. 5. The image encoding apparatus according to claim 4, wherein the output unit outputs the prediction coefficient for each class together with the correction data. 6. The prediction means comprises: a storage means for storing, for each predetermined class, a prediction coefficient for calculating the prediction value by a linear combination with the correction data; Classification means for classifying the correction data into any one of the predetermined classes, and prediction value calculation means for obtaining the prediction value from the prediction coefficient for the class of the correction data and the correction data. The image encoding device according to claim 1, wherein: 7. The prediction coefficient according to claim 6, wherein the prediction coefficient for each class stored in the storage unit is generated by performing learning using image data for learning. Image encoding device. 8. The image encoding apparatus according to claim 7, wherein the output unit outputs the prediction coefficient for each class together with the correction data. 9. The apparatus according to claim 1, wherein the correction unit includes a storage unit that stores a correction value for correcting the compressed data, and corrects the compressed data using the correction value. Item 2. The image encoding device according to Item 1. 10. The determination means determines whether the correction data is appropriate based on whether or not the prediction error is equal to or less than a predetermined value. The output means determines whether the correction error is equal to or less than a predetermined value. The image encoding device according to claim 1, wherein the correction data is output. 11. An image encoding method for encoding an image, comprising compressing an original image by reducing its number of pixels, and limiting a level of compressed data obtained by compressing the original image. Correcting the compressed data having a limited level, outputting corrected data, predicting the original image based on the corrected data, outputting a predicted value thereof, and calculating the predicted value for the original image. Calculating the prediction error of the correction data, and determining the appropriateness of the correction data based on the prediction error until the correction data becomes appropriate. An image coding method characterized by outputting as a result of conversion. 12. An image decoding apparatus for decoding encoded data obtained by encoding an image, comprising: a receiving unit that receives the encoded data; and a decoding unit that decodes the encoded data. The encoded data is obtained by compressing the original image by reducing the number of pixels, limiting the level of compressed data obtained by compressing the original image, and correcting the level-limited compressed data. And outputting correction data, predicting the original image based on the correction data, outputting a prediction value thereof, calculating a prediction error of the prediction value with respect to the original image, Determining whether the correction data is appropriate on the basis of the correction data until the correction data becomes appropriate. Device. 13. The decoding unit includes: a classifying unit that classifies the correction data into a predetermined class according to a property thereof; and a prediction value calculation unit that obtains the prediction value corresponding to the class. 13. The image decoding apparatus according to claim 12, wherein: 14. The coded data also includes a prediction coefficient for calculating the prediction value by a linear combination with the correction data, and the decoding unit calculates the prediction coefficient from the prediction coefficient and the correction data. 13. The image decoding apparatus according to claim 12, further comprising predicted value calculation means for obtaining a value. 15. The coded data also includes a prediction coefficient for each predetermined class for calculating the predicted value by a linear combination with the correction data, and the decoding unit converts the correction data into Classifying means for classifying the correction data into a predetermined class according to the property thereof; and prediction value calculating means for obtaining the prediction value from the prediction coefficient for the class of the correction data and the correction data. The image decoding apparatus according to claim 12, wherein 16. The decoding unit, comprising: a storage unit that stores, for each predetermined class, a prediction coefficient for calculating the prediction value by a linear combination with the correction data; Classifying means for classifying into any of the predetermined classes according to properties; reading out the prediction coefficient for the class of the correction data from the storage means; and calculating the prediction value from the prediction coefficient and the correction data. 13. The image decoding apparatus according to claim 12, further comprising: a predicted value calculating unit that calculates the predicted value. 17. The prediction coefficient according to claim 16, wherein the prediction coefficient for each class stored in the storage unit is generated by performing learning using image data for learning. Image decoding device. 18. An image decoding method for decoding encoded data obtained by encoding an image, wherein the encoded data is obtained by compressing an original image by reducing the number of pixels thereof, and Limiting the level of compressed data obtained by compression, correcting the level-limited compressed data, outputting correction data, predicting the original image based on the correction data, and predicting the original image. By outputting a value, calculating a prediction error of the predicted value with respect to the original image, and determining the appropriateness of the correction data based on the prediction error, by repeating until the correction data becomes appropriate. An image decoding method characterized by including the obtained corrected data that has become appropriate. 19. A transmission method for transmitting encoded data obtained by encoding an image, wherein the encoded data is obtained by compressing an original image by reducing the number of pixels thereof, and compressing the original image. Limiting the level of the compressed data obtained by the above, correcting the level-limited compressed data, outputting correction data, predicting the original image based on the correction data, and outputting the predicted value. And calculating a prediction error of the prediction value with respect to the original image, and determining whether the correction data is appropriate based on the prediction error, by repeating until the correction data becomes appropriate. And a transmission method including the corrected correction data. 20. A recording medium on which encoded data obtained by encoding an image is recorded, wherein the encoded data is obtained by compressing an original image by reducing the number of pixels, and compressing the original image. Limiting the level of the compressed data obtained by performing the correction, outputting the corrected data by correcting the level-limited compressed data, predicting the original image based on the corrected data, and predicting the predicted value. And calculating the prediction error of the predicted value with respect to the original image, and determining the correctness of the correction data based on the prediction error by repeating until the correction data becomes appropriate. A recording medium including the corrected correction data. 21. An image encoding apparatus for encoding an image, comprising: a classifying unit that classifies pixels constituting the image into a predetermined class according to a property thereof; and a predetermined mapping coefficient for each of the classes. By performing a predetermined operation using a target pixel of interest in the image and the mapping coefficient corresponding to the class of the target pixel, thereby obtaining the target An image encoding apparatus comprising: an arithmetic unit that calculates correction data obtained by correcting pixels; and a limiting unit that limits the level of the correction data to obtain encoded data obtained by encoding the image. 22. The image coding apparatus according to claim 21, wherein said calculation means reduces the number of pixels of said image by performing said predetermined calculation. 23. The method according to claim 22, wherein the calculating means performs the predetermined calculation using a plurality of pixels including the pixel of interest and the mapping coefficient corresponding to the class of the pixel of interest. The image encoding device according to claim 1. 24. The image encoding apparatus according to claim 22, wherein the mapping coefficient is generated by performing learning using image data for learning. 25. The mapping coefficient is obtained by performing learning such that a prediction error of a prediction result of predicting an original image from the encoded data with respect to the original image is minimized. The image encoding device according to claim 22, wherein: 26. The mapping coefficient obtained by performing learning so that a prediction error of a prediction result of predicting an original image from the encoded data with respect to the original image becomes a predetermined value or less. 23. The image encoding apparatus according to claim 22, wherein: 27. The mapping coefficient classifies pixels constituting an image for learning into one of the classes according to the nature of the pixel. By performing the predetermined operation using a learning pixel and a predetermined coefficient corresponding to the class of the target learning pixel, learning correction data for correcting the target learning pixel is calculated. Calculating learning limit data with a limited level; predicting a predicted value of the learning image based on the learning limited data; predicting a prediction error of the predicted value of the learning image with respect to the learning image; , And changing the predetermined coefficient based on the prediction error is repeated until the predetermined coefficient reaches an optimum value, and the predetermined coefficient of the optimum value is obtained. thing The image encoding device according to claim 22, wherein: 28. The mapping coefficient, comprising: compressing a learning image by reducing the number of pixels thereof; limiting a level of compressed learning data obtained by compressing the learning image; Correcting the limited training compressed data of
Outputting learning correction data, predicting the learning image based on the learning correction data, outputting a prediction value thereof, and calculating a prediction value of the learning image with respect to the learning image. Calculating the prediction error, and determining the appropriateness of the learning correction data based on the prediction error, until the learning correction data becomes appropriate. 23. The image coding apparatus according to claim 22, wherein the image coding apparatus is obtained using the learning correction data and the learning image. 29. An image encoding method for encoding an image, comprising: classifying pixels constituting the image into a predetermined class according to a property thereof; and storing a predetermined mapping coefficient for each class. By reading the mapping coefficient corresponding to the class of the pixel of interest in the image from the mapping coefficient storage means, and performing a predetermined operation using the mapping coefficient and the pixel of interest. An image encoding method comprising: calculating correction data obtained by correcting the target pixel; and limiting the level of the correction data to obtain encoded data obtained by encoding the image. 30. An image decoding device that decodes encoded data obtained by encoding an image, comprising: a receiving unit that receives the encoded data; and a decoding unit that decodes the encoded data. The encoded data classifies the pixels constituting the image into one of first classes according to the properties thereof, and stores a predetermined mapping coefficient for each of the first classes. Reading, from the mapping coefficient storage means, the mapping coefficient corresponding to the first class of the pixel of interest in the image, and performing a predetermined operation using the mapping coefficient and the pixel of interest; , Calculating correction data obtained by correcting the target pixel, and obtaining the correction data by limiting the level of the correction data. 31. The coded data obtained by reducing the number of pixels of the image.
5. The image decoding device according to claim 1. 32. The decoding means, comprising: a prediction coefficient storage means for storing a prediction coefficient for calculating the prediction value by a linear combination with the encoded data for each second class; Classifying means for classifying coded data into one of the second classes according to the property thereof; and reading out the prediction coefficients of the coded data for the second class from the prediction coefficient storage means. 32. A predictive value calculating means for calculating the predictive value from the predictive coefficient and the encoded data.
5. The image decoding device according to claim 1. 33. The image decoding apparatus according to claim 32, wherein the prediction coefficient is generated by performing learning using image data for learning. 34. The image decoding apparatus according to claim 31, wherein the mapping coefficient is generated by performing learning using learning image data. 35. The mapping coefficient is obtained by performing learning so that a prediction error of a prediction result of predicting an original image from the encoded data with respect to the original image is minimized. The image decoding device according to claim 31, wherein: 36. The mapping coefficient obtained by performing learning so that a prediction error of a prediction result of predicting an original image from the coded data with respect to the original image becomes a predetermined value or less. 32. The image decoding device according to claim 31, wherein: 37. An image decoding method for decoding coded data obtained by coding an image, wherein the coded data is obtained by converting pixels constituting the image into pixels of a predetermined class according to a property thereof. The mapping coefficient corresponding to the class of the pixel of interest in the image is read out from the mapping coefficient storage unit that stores a predetermined mapping coefficient for each class. By performing a predetermined operation using the mapping coefficient and the target pixel, correction data obtained by correcting the target pixel is calculated, and the correction data is obtained by limiting the level of the correction data. Image decoding method. 38. A transmission method for transmitting coded data obtained by coding an image, wherein the coded data classifies pixels constituting the image into a predetermined class according to a property of the image. In each case, the mapping coefficient corresponding to the class of the pixel of interest in the image is read out from the mapping coefficient storage unit storing the predetermined mapping coefficient, and the mapping coefficient and the pixel of interest are read out. , A predetermined operation is performed to calculate correction data obtained by correcting the target pixel, and the correction data is obtained by limiting a level of the correction data. 39. A recording medium on which encoded data obtained by encoding an image is recorded, wherein the encoded data classifies pixels constituting the image into a predetermined class according to their properties, For each of the classes, the mapping coefficient corresponding to the class of the pixel of interest in the image is read out from the mapping coefficient storage unit that stores a predetermined mapping coefficient. A recording medium, which is obtained by performing a predetermined operation using a pixel to calculate correction data for correcting the target pixel, and limiting the level of the correction data.