JP4482502B2 - Information signal conversion apparatus and method - Google Patents

Description

The present invention relates to an information signal conversion apparatus and method that can further improve, for example, resolution, signal-to-noise ratio, compression distortion, and luminance value of an image signal and / or an audio signal by using class classification adaptive processing.

  Conventionally, class classification adaptive processing has been applied, such as SD (Standered Definition) to HD (High Deginittion) image information conversion device, spatio-temporal model coding, MUSE image quality improvement, Y / C separation of composite signals, etc. Application ideas have been proposed. That is, a space-time pixel of a certain size is blocked, and this is classified into classes by some method, for example, ADRC (Adaptive Dynamic Range Coding), and modeled by linear linear combination for each class, that is, a prediction formula is established, Optimal data can be obtained by calculating the coefficients and pixels stored for each class. At this time, the coefficients stored for each class are obtained by learning in advance using a least square method or the like.

  As described above, when processing such as resolution compensation is performed using the class classification adaptive processing, a certain degree of effect can be obtained. The present invention relates to an improvement of the above-described class classification adaptive processing.

That is, an object of the present invention is to perform some processing at the time of learning in order to obtain a coefficient used for the classification adaptation process, and use the coefficient, thereby further increasing the resolution, SN ratio, compression distortion, and luminance value. An object of the present invention is to provide an information signal conversion apparatus and method that can be improved.

To achieve the above object, the present invention is an input information signal, the first information signal converter for converting the output information signal of the second sampling frequency has higher than the sampling frequency of the input information signal,
Subjected to low-quality processing with respect to the first sampling frequency of the information signal, a first first-class generating means for generating a first class information from the low have a third sampling frequency of the information signal than the sampling frequency When,
For each first class information, between the third and the information signal of the sampling frequency, the first fourth sampling frequency information signals have low than high rather and second sampling frequency than the sampling frequency, Coefficient computing means for obtaining a coefficient by learning based on a least square error criterion in advance;
Storage means for storing the coefficient obtained by the coefficient calculation means;
Second class generating means for generating second class information from the input information signal;
An information signal conversion apparatus comprising: a calculation means for reading a coefficient from the storage means in response to the second class information and converting the coefficient into an output information signal by calculation processing with the input information signal.

Further, the present invention is an input information signal, the information signal conversion method for converting a first output information signal of the second sampling frequency has higher than the sampling frequency of the input information signal,
Subjected to low-quality processing with respect to the first sampling frequency of the information signal, a first class generation step of generating a first class information from the first third of the sampling frequency of the information signal have lower than the sampling frequency When,
For each first class information, between the third and the information signal of the sampling frequency, the first fourth sampling frequency information signals have low than high rather and second sampling frequency than the sampling frequency, A coefficient calculation step for obtaining a coefficient by learning based on a least square error criterion in advance;
A storage step of storing in the storage means the coefficient obtained in the coefficient calculation step;
A second class generation step of generating second class information from the input information signal;
An information signal conversion method comprising: an operation step of reading a coefficient from the storage means in response to the second class information and converting the coefficient into an output information signal by an operation process with the input information signal.

  In the present invention, processing f (x) such as LPF (low-pass filter) processing, compression / expansion, gain, and / or offset is performed on the input image signal as low image quality processing. In the case of the processing f (x), for example, LPF, the input image signal subjected to LPF has a sampling rate reduced to, for example, ½, and a coefficient is obtained based on the least square method error norm with the teacher signal. In the class classification adaptation process using the obtained coefficient, the input signal is converted into a signal whose resolution is improved by a factor of 4, and then output.

  According to the present invention, learning is performed using a signal generated by performing processing such as LPF, compression / decompression, gain, and / or offset, and a teacher signal as processing for reducing image quality on an input signal. Do and thereby get the coefficient. The class classification adaptive processing using this coefficient can further improve the resolution, SN ratio, compression distortion, and luminance value.

  Further, according to the present invention, when an input signal is processed using a coefficient acquired by learning using a high-quality teacher signal, the information signal has a high image quality that exceeds the teacher signal. Furthermore, even when the input signal is processed using a coefficient acquired by learning using a teacher signal whose image quality is not specified, for example, the input signal is further improved. It can be converted into an information signal with high image quality.

  The present invention will be described below with reference to the drawings. FIG. 1 is a block diagram showing a general configuration of a learning apparatus for class classification adaptation processing according to the present invention. 1 is supplied to the processing circuit 3, the input terminal B of the switch 4, and the input terminal D of the switch 6. The teacher signal supplied from the input terminal 2 is supplied to the input terminal A of the switch 4. At this time, the input signal and the teacher signal may be the same signal, or processing such as down-sampling the teacher signal may be performed so as to be connected by a dotted line in the figure.

  In the processing circuit 3, as will be described later, as the processing f (x) to be performed on the input signal, for example, low image quality reduction processing such as LPF processing, compression / expansion, gain and / or offset is performed. An example of compression and decompression is an MPEG2 encoder and decoder. The correction target signal that has been subjected to the processing f (x) is supplied to the class generation circuit 5 and the input terminal C of the switch 6. The class generation circuit 5 generates class information (hereinafter referred to as an index) from the signal subjected to the processing f (x), and this index is supplied to the learning circuit 7.

  In the above configuration, when the switch 4 selects the input terminal A and the switch 6 selects the input terminal C, the learning circuit 7 performs the teacher signal from the input terminal 2 and the process f (x). Learning based on the least square method error criterion is performed from the received input signal, and a linear linear combination coefficient is obtained. The coefficient is obtained by obtaining the minimum value of the square of the error between the teacher signal and the input signal subjected to the processing f (x). Further, when the switch 4 selects the input terminal A and the switch 6 selects the input terminal D, the learning circuit 7 learns from the teacher signal from the input terminal 2 and the input signal from the input terminal 1. When the switch 4 selects the input terminal B and the switch 6 selects the input terminal C, the input signal from the input terminal 1 as the teacher signal and the input signal subjected to the processing f (x) Learning is done from.

  The coefficient acquired by the learning circuit 7 is output from the output terminal 8 and stored in a storage medium such as a memory (not shown). FIG. 1 shows a general configuration for generating a class from a signal obtained by performing some processing f (x) on an input signal, and learning based on the least square method error criterion from the input signal and a teacher signal. Is shown. As described above, in this block diagram, when the input terminal A of the switch 4 and the input terminal D of the switch 6 are selected, there are paths for learning using the teacher signal and the input signal as in the conventional case. Secured.

  A general configuration of the class classification adaptive processing using the obtained coefficients is shown in FIG. An input signal supplied from the input terminal 11 is supplied to the class generation circuit 12 and the delay (DL) circuit 14. The class generation circuit 12 generates a class of the supplied input signal, and the generated class is supplied to the coefficient memory 13 in which the coefficient obtained by learning is stored. In the coefficient memory 13, the coefficient is read in response to the class from the class generation circuit 12, and the read coefficient is supplied to the prediction calculation circuit 15. In the predictive operation circuit 15, optimal data is obtained by the operation of the input signal and the coefficient delayed by a predetermined time by the delay circuit 14.

  In the general configuration of the present invention described above, the first embodiment in which the processing circuit 3 performs the LPF processing and thereby increases the resolution will be described with reference to FIG. First, video signals captured by two types of imaging systems with different resolutions are used for the same subject. At this time, an input signal obtained by an imaging system having a lower resolution is converted from an analog signal to a digital signal by an A / D converter having a sampling frequency of 13.5 MHz, for example, and this video signal is represented as LS. The teacher signal obtained by the higher-resolution imaging system is converted from an analog signal to a digital signal by an A / D converter having a sampling frequency of 27.0 MHz, for example, and this video signal is represented as HS.

  Thus, using the input signal (video signal LS) and the teacher signal (video signal HS), for example, in FIG. 1 described above, the input terminal A of the switch 4 and the input terminal D of the switch 6 are connected. By learning, a coefficient for generating a signal having a sampling rate twice as high as that from a signal having a low sampling rate is calculated.

  Further, by learning with the input terminal A of the switch 4 and the input terminal C of the switch 6 in FIG. 1 connected, a coefficient for generating a signal with a sampling rate four times higher from a signal with a lower sampling rate. Is calculated. This is because the coefficient generated by learning is subjected to, for example, LPF processing as processing f (x) on the input signal, and the number of pixels of the supplied input signal is not reduced, but only the bandwidth of the input signal is reduced. Further, as shown in FIG. 3A, for example, a signal (LPF-LS) with a sampling rate reduced to ½ and a teacher signal are used.

  More specifically, the coefficient obtained by learning the signal LPF-LS (6.75 MHz) obtained by performing LPF on the input signal LS (13.5 MHz) and the teacher signal HS (27.0 MHz) is obtained. It can be used to generate a four times higher sampling rate signal (54.0 MHz) from a lower sampling rate input signal LS (13.5 MHz), as shown in FIG. 3B.

  In the conventional learning method, it was only possible to obtain twice the resolution of the input signal from the input signal. However, the processing for increasing the resolution using the coefficient obtained by the learning method to which this embodiment is applied is performed. When performing, it is possible to create a signal having a resolution four times that of an input signal having a normal sampling rate.

  Next, learning in the case of improving the SN ratio will be described as a second embodiment. Generally, when a video signal photographed by a camera passes through a transmission system or a recording / reproducing system, some noise is added, and the SN ratio is deteriorated. Usually, a coefficient for improving the S / N ratio is learned by using a camera output as a teacher signal and a signal passing through a transmission system or a recording / reproducing system as an input signal. When using this coefficient to improve the SN, if an unexpected amount of noise is added to the input signal, a significant improvement cannot be desired.

  Therefore, in the second embodiment, a random number capable of controlling characteristics such as size and occurrence frequency is generated, for example, by a computer as processing f (x) to an input signal that is a camera output, and added to the artificial signal. A signal contaminated with typical noise is generated, and a coefficient for improving the SN ratio is calculated by learning with a teacher signal. By doing so, it becomes possible to easily control the learning of the coefficient with respect to the assumed noise characteristics, and the improvement effect can be increased. Note that it is expected that the effect of improving the S / N ratio will be greatly improved by learning together with signals that have passed through conventional transmission systems and recording / reproducing systems.

  Next, learning in the case of improving compression distortion will be described as a third embodiment. In the case of transmitting an image through the Internet or the like, since the image is usually transmitted after being compressed, the image reproduced on the computer monitor often has a unique distortion. Also, in TV broadcasting, not only uncompressed image signals but also images compressed by MPEG or the like have been handled, and image distortion due to compression has become a problem.

  The image distortion due to this compression generally differs depending on the compression method. Therefore, in the third embodiment, the input image signal is subjected to compression / decompression processing of various assumed compression methods as the processing f (x), and the uncompressed image signal which is a teacher signal. And learning the coefficient for improving the compression distortion.By performing the class classification adaptive processing using the acquired coefficient for any unknown input signal, the compression distortion can be reduced. Improvements can be made.

  Next, learning for performing luminance correction will be described as a fourth embodiment. FIG. 4 shows a more specific block diagram of the learning device for class classification adaptation processing shown in FIG. 1 for luminance correction. Of the image signal supplied from the input terminal 21, the luminance signal Y is converted from an analog signal to a digital signal by the A / D converter 22. In the A / D converter 22, for example, when sampling is performed with a clock of 13.5 MHz, the image size is about 720 pixels wide × 480 lines high per frame. The luminance signal converted into the digital signal is supplied from the A / D converter 22 to the learning circuit 23 and the gain / offset circuit 24.

  In the gain / offset circuit 24, a process for changing the gain and / or offset of the supplied luminance signal is executed as the process f (x). The gain is related to the brightness (contrast) of the illumination, and the offset is related to the average brightness of the illumination. At this time, the gain does not always have to be 1, and may be 0.9, 1.2, or the like. The luminance signal subjected to the gain and / or offset processing is supplied to the average value / standard deviation circuit 25 and the delay circuit 27.

  In the average value / standard deviation circuit 25, for example, an average value and a standard deviation per field or frame of the supplied luminance value are obtained as described later. The average value / standard deviation circuit 25 has a table for obtaining a frequency distribution for each luminance value, and an average luminance value is calculated as shown in FIG. 5 from the frequency distribution obtained by multiplying one field period or one frame period. In addition, the standard deviation is also calculated. The calculated average value and standard deviation are supplied from the average value / standard deviation circuit 25 to the quantization (Q) circuit 26. A formula for calculating the average value of luminance is shown in Formula (1), and a formula for calculating the standard deviation is shown in Formula (2).

Average value = Σ (luminance value × frequency) / total frequency (1)
Standard deviation = √ (Σ ((luminance value−average value) ² × frequency) / total frequency) (2)
However, √ () is the square root of the calculation result in ().

  In the quantization circuit 26, the calculated average value and standard deviation are quantized with a bit and b bit, respectively, and a code of total n bits (n = a + b) is generated. This n-bit code is supplied from the quantization circuit 26 to the degenerate ROM 32. Furthermore, the n-bit code is a pattern of the so-called luminance distribution. By looking at this, it can be determined whether the luminance distribution is biased toward darker or brighter, and whether the luminance distribution is flat or steep. can do.

  On the other hand, in the delay circuit 27, the delay is performed for the time until the n-bit code is generated (1 field or 1 frame + α), and the output is supplied to the blocking circuits 28 and 30 and further to the delay circuit 33. The In the blocking circuit 28, a plurality of pixels in the space around the pixel of interest are selected and blocked. The blocked pixels are supplied to the ADRC circuit 29. In the ADRC circuit 29, the maximum value and the minimum value are selected from the blocked pixels as will be described later, and each pixel is requantized to generate an m-bit code, which is supplied to the degenerate ROM 32. This code is a pattern of how the brightness of a space changes. Class classification based on an m-bit code has an effect on improvement of S / N ratio, resolution, etc., rather than luminance correction itself.

  In the blocking circuit 30, a plurality of pixels in the space around the target pixel to be corrected are selected and blocked. The blocking executed in the blocking circuit 30 may be different from the blocking executed in the blocking circuit 28 described above. That is, there is no problem even if the pixel selected in the blocking circuit 28 and the pixel selected in the blocking circuit 30 are different. The blocked pixels are supplied from the blocking circuit 30 to the averaging circuit 31. In the averaging circuit 31, the average value of luminance near the target pixel is calculated, and the calculated average value is shifted, quantized to p (<8) bits, and supplied to the degenerate ROM 32.

  In this way, the average value of the luminance of each block pixel is calculated, that is, by making the luminance level one of the class classifications, it is possible to change the way of correction in the level direction. For example, it is possible to correct only a bright part and a dark part, or to perform correction in consideration of γ characteristics. In addition, the effect of averaging the luminance level also serves to prevent the luminance correction from being overly sensitive.

  In the above description, three types of class classification codes have been generated. If these are simply combined, the number of classifications becomes enormous, and the capacity of a coefficient ROM (to be described later) becomes enormous. Therefore, the n bits from the quantization circuit 26, the m bits from the ADRC circuit 29, and the p bits from the averaging circuit 31 degenerate the supplied number of bits in the degeneration ROM 32. Specifically, the degenerate ROM 32 generates a class code (index) that is reduced from (n + m + p) bits to q bits in order to degenerate classes. Thus, a q-bit class code is finally generated from the degenerate ROM 32, and the q-bit class code is supplied to the learning circuit 23.

  Although the details of the degeneration method will not be described here, a method for collectively reducing a coefficient having a small norm as a vector quantization method from coefficient sets corresponding to all classes learned with (n + m + p) bits. Etc. shall be used. That is, the distance between the corresponding coefficients (the norm between coefficients) is obtained between the two coefficient sets, and the coefficient sets are collected based on the distance.

  The output of the delay circuit 33 for performing the delay adjustment is supplied to the blocking circuit 34, and the blocking circuit 34 blocks a plurality of pixels around the target pixel. Each pixel value formed into blocks is supplied to the learning circuit 23.

  Then, as described above, the teacher image signal from the A / D converter 22 is supplied to the learning circuit 23. The learning circuit 23 forms an n-tap linear temporary combination model, and the learning circuit 23 calculates each coefficient thereof. Each calculated coefficient is taken out from the output terminal 35 and stored in the coefficient ROM. The learning circuit 23 learns the coefficient for each class according to the least square method error criterion described later. The coefficient obtained by learning is output via the output terminal 35 and stored for each class in a storage medium such as a memory.

  Here, an example of the average value / standard deviation circuit 25 will be described with reference to FIG. A luminance value is supplied from the input terminal 41. The supplied luminance value is supplied to the luminance frequency distribution table 42. In the luminance frequency distribution table 42, for example, a frequency distribution table of luminance levels in one field or one frame is generated. Based on the generated table, the average value calculation circuit 43 calculates the average value by the equation (1), and the calculated average value is supplied to the standard deviation calculation circuit 44 and taken out from the output terminal 45. . In the standard deviation calculation circuit 44, the standard deviation is calculated from the frequency distribution table and the average value by the equation (2), and the calculated standard deviation is taken out from the output terminal 46. When the extracted standard deviation is small, the frequency distribution is narrow, and when the standard deviation is large, the frequency distribution is wide.

  An example of the configuration of the ADRC circuit 29 will be described with reference to FIG. Blocked data is supplied from the input terminal 51. The supplied data is supplied to the maximum value detection circuit 52, the minimum value detection circuit 53, and the delay circuit 54. The maximum value detection circuit 52 detects the maximum pixel value in the block, and the minimum value detection circuit 53 detects the minimum pixel value in the block. In the subtractor 55, the minimum value is subtracted from the maximum value, and the dynamic range DR of the block is calculated. The calculated dynamic range DR is supplied to the adaptive requantization circuit 57.

  In the delay circuit 54, each of the maximum value detection circuit 52 and the minimum value detection circuit 53 is delayed in time for detection, and is output pixel by pixel. In the subtracter 56, the minimum value is subtracted from each blocked pixel, and the subtracted value is supplied to the adaptive requantization circuit 57. The adaptive requantization circuit 57 quantizes the subtraction value for each pixel using a predetermined quantization step width corresponding to the dynamic range DR. In the parallelization circuit 58, the quantized pixels are parallelized in units of blocks and output as encoded data from the output terminal 59.

  As a learning method, a least square method for obtaining a relationship between pixel values of a large number of input signals for correction and pixel values of a teacher image signal is employed. First, it is assumed that there is a linear linear coupling relationship between the above-described values, and a linear linear coupling model is shown below.

Linear linear combination model: (observation equation)
XW = Y (3)

Solving method using the method of least squares: (residual equation)

を最小にする条件、すなわち To find the most probable value for each w _i from equation (5),

Which minimizes

なる、Ｎ個の条件を入れてこれを満足するｗ₁、ｗ₂、・・・、ｗ_Nを見いだせばよい。式（５）より、

It is only necessary to find w ₁ , w ₂ ,..., W _N that satisfy N conditions. From equation (5)

となり、式（６）条件をｉ＝１，２，・・・，Ｎについて立てればそれぞれ、

If the condition of formula (6) is established for i = 1, 2,..., N, respectively,

が得られる。ここで、式（５）および式（８）から次式の正規方程式が得られる。

Is obtained. Here, the following normal equation is obtained from the equations (5) and (8).

これは、ちょうど未知数の数Ｎ個だけある連立方程式であるから、これより最確値たる各ｗ_iを求めることができる。

Since this is a simultaneous equation with just N unknown numbers, each w _i which is the most probable value can be obtained.

のマトリクスが正則であれば解くことができる。（ただし、ｋ＝１，２，・・・，Ｎ、ｌ＝１，２，・・・，Ｎ）実際には、Gauss-Jordanの消去法（掃き出し法）を用いて連立方程式を解くことになる。 To be exact, it takes w _i in equation (9)

If the matrix is regular, it can be solved. (Where k = 1, 2,..., N, l = 1, 2,..., N) In practice, the simultaneous equations are solved using Gauss-Jordan elimination (sweeping method). Become.

Next, FIG. 8 shows a block diagram of hardware that performs the operation of the method of least squares. In the learning block diagram of FIG. 4, pixel values x _{1 to} x _{N of} a block centering on a pixel to be corrected and a teacher pixel value y corresponding to the pixel are input, and a class code (index) is input. Is entered. The circuit of the least square method is roughly divided into a normal equation generation circuit 61 and a CPU 62, and the normal equation generation circuit 61 includes a multiplier array 63, an addition memory 64, and a decoding unit 65. The CPU 62 is composed of, for example, a CPU that performs a sweep-out method to obtain the coefficient. The multiplier array 63 has a set of memories (or registers) for the target pixel position, and the addition memory 64 has a set of memories (or registers) corresponding to the number of classes. The decoding unit 65 decodes the supplied class code (index).

  Here, the multiplier array 63 will be described with reference to FIG. The teacher pixel value y corresponding to the pixel value of the block centered on the pixel to be corrected is multiplied by each element in the multiplier array 63 of the normal equation generation circuit 61 as shown in the figure. The result is supplied to the addition memory 64.

As shown in FIG. 10, the addition memory 64 includes an adder array 71 and memory (or register) arrays 72 _{1 to} 72 _N. The adder array 71 is supplied with the result from the multiplier array 63 and the outputs from the memory (or register) arrays 72 _{1 to} 72 _N. The addition result is output from the adder array 71 to the memory (or register) arrays 72 _{1 to} 72 _N. At this time, which memory (or register) array 72 _{1 to} 72 _N is selected is uniquely determined by decoding the class code (index) supplied to the decoding unit 65. That is, the memory (or register) array 72 is selected for each class determined by the index. In the selected memory (or register) array 72, the result of the product-sum operation is updated and stored.

の位置に対応する。正規方程式（９）を見てわかるように右上の項を反転すれば左下と同じものになるため、各アレイは三角形の形状をしている。 Note that the position of each array depends on w _i of the normal equation (9).

Corresponds to the position of. As can be seen from the normal equation (9), if the upper right term is inverted, it becomes the same as the lower left, so each array has a triangular shape.

  As described above, the product-sum operation is performed for each class during a certain period, and a normal equation for each class for each pixel position is generated. The result of each term of the normal equation for each class is stored in the memory (or register) array corresponding to each class, and then each term of the normal equation for each class is supplied to the calculation circuit of the sweep method. Is done. This calculation is performed by the CPU 62. The calculated set of coefficients is used by being written in a coefficient table composed of a coefficient ROM.

  Next, a luminance correction circuit capable of improving the resolution of an image signal using the coefficient acquired by the learning described above will be described with reference to FIG. In the description of this example, the same reference numerals are given to the same portions as those in the fourth embodiment, and the description thereof is omitted.

The luminance signal Y supplied from the input terminal 81 is supplied to the A / D converter 82. The A / D converter 82 samples, for example, 13.5 MHz, and the digitized signal has an average value / standard deviation. It is output to the circuit 25 and the delay circuit 27. In the degeneration ROM 32, the n-bit code from the quantization circuit 26, the m-bit code from the ADRC circuit 29, and the p-bit code from the averaging circuit 31 are degenerated to generate a q-bit class code (index). ) Is supplied to the coefficient ROM 83. The coefficient ROMs 83 _{1 to} 83 _N are addressed with the supplied class code, and the coefficients w _{1 to} w _N are read out. The read coefficients w _{1 to} w _N are supplied to multipliers 84 _{1 to} 84 _N , respectively.

In the blocking circuit 34, a plurality of pixels around the pixel of interest are blocked. Each of the blocked pixel values is supplied to multipliers 84 _{1 to} 84 _N. The multiplier 84 ₁ -84 _N, and the coefficients w ₁ to w _N from the coefficient ROM 83 ₁ to 83 _N, each pixel value is blocked are multiplied and the multiplied value is supplied to the adder 85. In the adder 85, the multiplication values from the multipliers 84 _{1 to} 84 _N are added. That is, in the multipliers 84 _{1 to} 84 _N and the adder 85, the brightness correction value is predicted by performing a product-sum operation. The predicted value is D / A converted in the D / A converter 86 and taken out from the output terminal 87 as the corrected luminance value Y ′.

Coefficients w ₁ to w _N to be read from the coefficient ROM 83 ₁ to 83 _N, depending video signal of the signal LPF-LS and 27.0FMHz sampling rate is 6.75MHz when learning described above, and the resulting coefficients. By using this coefficient when generating the prediction value, the luminance signal Y having a sampling rate of 13.5 MHz can generate a luminance signal Y ′ having a sampling rate of 54.0 MHz, which is four times higher.

  In this embodiment, a method for realizing all the hardware is described. However, the digitized data may be calculated by software by taking it into a computer.

  Further, in this embodiment, when learning for obtaining coefficients for improving resolution, S / N ratio, compression distortion, and luminance value, processing f (x) to be performed on the input signal is performed separately. However, two or more of these processes may be combined. For example, after gain and / or offset, LPF processing is performed on the input signal so that the resulting coefficients can be used to further improve resolution and brightness values.

  The present invention is not limited to the above-described embodiment of the present invention, and various modifications and applications are possible without departing from the spirit of the present invention.

It is a block diagram which shows the general structural example of the learning apparatus of the class classification adaptive process of this invention. It is a block diagram which shows the general structural example of one Example of the class classification adaptation process of this invention. It is a basic diagram of an example used for description of the resolution of this invention. It is an Example of the learning circuit corresponding to the luminance correction apparatus which can apply this invention. It is a frequency distribution table used for description of the Example which can apply this invention. It is an example of the average value / standard deviation circuit used in the brightness correction apparatus to which the present invention is applied. It is an example of the ADRC circuit used for the brightness correction apparatus to which this invention is applied. It is a block diagram for demonstrating an example of the least squares method applied to the learning circuit based on this invention. It is a basic diagram of an example used for description of the multiplier array which concerns on this invention. It is a basic diagram of an example used for description of the addition memory which concerns on this invention. It is a block diagram which shows the Example of the brightness correction apparatus which can apply this invention.

Explanation of symbols

DESCRIPTION OF SYMBOLS 3 ... Processing circuit 4, 6 ... Switch 5 ... Class generation circuit 7 ... Learning circuit

Claims (4)

An input information signal, the information signal converter for converting the first output information signal of the second sampling frequency has higher than the sampling frequency of the input information signal,
First class that generates the applied low quality process on the first sampling frequency of the information signal, the first class information from the first third of the sampling frequency of the information signal have lower than the sampling frequency Generating means;
The respective first class information, and the information signal of the third sampling frequency, the first information signal of the fourth sampling frequencies have lower than the high rather and said second sampling frequency than the sampling frequency and Coefficient calculating means for obtaining a coefficient by learning based on the least square error criterion in advance,
Storage means for storing the coefficient obtained by the coefficient calculation means;
Second class generating means for generating second class information from the input information signal;
An information signal conversion comprising: an arithmetic means for reading the coefficient from the storage means in response to the second class information and converting the coefficient into the output information signal by arithmetic processing with the input information signal apparatus. Said coefficient calculating means, for each of the first class information, the information signal of the fourth sampling frequency, the plurality of information signals of the third sampling frequency, said plurality of information signals of the third sampling frequency more as the linear combination equation holds, the a plurality of information signals of the third sampling frequency, a plurality of information signals of the third sampling frequency in a linear combination formula for calculating the coefficients for calculating the target And calculating a plurality of information signals having the third sampling frequency so that an error between the calculation result and the information signal having the fourth sampling frequency is approximately zero. The information signal conversion apparatus according to claim 1, wherein the coefficient for an object is obtained. Furthermore, a processing means for generating said performing low-quality processing with respect to the first sampling frequency of the information signal, the first information signal of the third sampling frequency have lower than the sampling frequency,
To claim 1, characterized in that it comprises an input means for information signals of the fourth sampling frequency of the low There sampling frequency is input than high rather and said second sampling frequency than the first sampling frequency The information signal converter described. An input information signal, the information signal conversion method for converting a first output information signal of the second sampling frequency has higher than the sampling frequency of the input information signal,
First class that generates the applied low quality process on the first sampling frequency of the information signal, the first class information from the first third of the sampling frequency of the information signal have lower than the sampling frequency Generation step;
The respective first class information, and the information signal of the third sampling frequency, the first information signal of the fourth sampling frequencies have lower than the high rather and said second sampling frequency than the sampling frequency and A coefficient calculation step for obtaining a coefficient by learning based on a least square error criterion in advance,
A storage step for storing in the storage means the coefficient obtained in the coefficient calculation step;
A second class generation step of generating second class information from the input information signal;
An information signal conversion comprising: a calculation step of reading the coefficient from the storage means in response to the second class information and converting the coefficient into the output information signal by a calculation process with the input information signal Method.

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