JP2001320711A - Data processor, data processing method and recording medium - Google Patents

Abstract

PROBLEM TO BE SOLVED: To efficiently decode JPEG-coded data. SOLUTION: JPEG-coded data are made into DCT coefficients (quantized DCT coefficients) quantized by entropy-decoding, and a quantization table used for JPEG coding is separated from the coded data. A forecasting tap extracting circuit 41 and a class tap extracting circuit 42 extract a necessary coefficient from the quantized DCT coefficients, and compose the forecasting tap and the class tap, respectively. A class classification circuit 43 performs class classification based on the class tap and the quantization table, and a coefficient table storage part 44 supplies a tap coefficient corresponding to the class obtained as a result of the class classification to a product-sum operation circuit 45. The product-sum operation circuit 45 performs a linear predictive operation by using the tap coefficient and the class tap, and obtains the decoded image data.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a data processing apparatus, a data processing method, and a recording medium, and more particularly, to a data processing apparatus and a data processing method suitable for decoding, for example, lossy-compressed images. And a recording medium.

[0002]

2. Description of the Related Art For example, digital image data has a large data amount.
A large-capacity recording medium and transmission medium are required. Therefore, in general, recording and transmission are performed after compressing and encoding image data to reduce the data amount.

As a method of compressing and encoding an image, for example, a JPEG (Joint Photo) which is a compression encoding method of a still image is used.
There is an MPEG (Moving Picture Experts Group) method, which is a moving image compression encoding method, and the like.

For example, encoding / decoding of image data according to the JPEG system is performed as shown in FIG.

[0005] That is, FIG. 1A shows a configuration of an example of a conventional JPEG encoding device.

[0006] The image data to be encoded is input to a blocking circuit 1, which divides the input image data into blocks of 64 8x8 pixels. Each block obtained by the blocking circuit 1 is supplied to a DCT (Discrete Cosine Transform) circuit 2. The DCT circuit 2 performs a DCT (Discrete Cosine Transform) process on the block from the blocking circuit 1, and performs one DC (Direct Current) component and 63 frequency components (horizontal and vertical). AC (Alter
(Nating Current) component) into a total of 64 DCT coefficients. The 64 DCT coefficients for each block are D
The signal is supplied from the CT circuit 2 to the quantization circuit 3.

The quantization circuit 3 quantizes the DCT coefficient from the DCT circuit 2 according to a predetermined quantization table, and uses the quantization result (hereinafter, appropriately referred to as a quantized DCT coefficient) for quantization. The information is supplied to the entropy encoding circuit 4 together with the quantization table.

FIG. 1B shows an example of a quantization table used in the quantization circuit 3. Generally, quantization tables take into account human visual characteristics,
The low-frequency DCT coefficients of high importance are finely quantized,
A quantization step for coarsely quantizing the DCT coefficient of a low-frequency high frequency is set, and thereby the image quality of the image is suppressed from being degraded, and the compression is performed efficiently.

FIG. 1B shows two types of quantization tables, but the number of quantization tables used for JPEG encoding is not limited to two. Note that what kind of quantization table is used is determined based on the compression ratio set by the user.

The entropy coding circuit 4 performs entropy coding processing such as Huffman coding on the quantized DCT coefficient from the quantization circuit 3 and adds a quantization table from the quantization circuit 3. Then, the resulting encoded data is output as a JPEG encoded result.

Next, FIG. 1 (C) shows the JPE of FIG. 1 (A).
1 shows a configuration of an example of a conventional JPEG decoding device that decodes encoded data output from a G encoding device.

The encoded data is supplied to an entropy decoding circuit 1
1, the entropy decoding circuit 11 separates the encoded data into entropy-encoded quantized DCT coefficients and a quantization table. Further, the entropy decoding circuit 11 performs the entropy-encoded quantization D
The CT coefficients are entropy-decoded, and the resulting quantized DCT coefficients are supplied to an inverse quantization circuit 12 together with a quantization table. The inverse quantization circuit 12 inversely quantizes the quantized DCT coefficient from the entropy decoding circuit 11 in accordance with the quantization table from the entropy decoding circuit 11, and converts the resulting DCT coefficient into the inverse DCT coefficient.
It is supplied to the circuit 13. The inverse DCT circuit 13 performs an inverse DCT process on the DCT coefficient from the inverse quantization circuit 12, and supplies the resulting 8 × 8 pixel decoded block to the block decomposition circuit 14. The block decomposition circuit 14 calculates the inverse D
By unblocking the decoded block from the CT circuit 13, a decoded image is obtained and output.

[0013]

SUMMARY OF THE INVENTION The JPEG shown in FIG.
In the encoding device, the quantization circuit 3 can reduce the data amount of the encoded data by increasing the quantization step of the quantization table used for quantizing the block. That is, high compression can be realized.

However, when the quantization step is increased, the so-called quantization error is also increased.
(C) The image quality of the decoded image obtained by the JPEG decoding device is degraded. That is, blur, block distortion, mosquito noise, and the like appear conspicuously in the decoded image.

Therefore, in order to reduce the data amount of the encoded data and not to deteriorate the image quality of the decoded image, or to maintain the data amount of the encoded data and improve the image quality of the decoded image. It is necessary to perform some kind of processing for improving image quality after JPEG decoding.

However, performing a process for improving the image quality after JPEG decoding complicates the process.
The time until a decoded image is finally obtained also increases.

The present invention has been made in view of such circumstances, and is intended to efficiently obtain a high-quality decoded image from a JPEG-encoded image or the like. .

[0018]

A first data processing device according to the present invention comprises:
Classifying means for classifying the target data of interest into one of several classes, and a tap coefficient for each predetermined class obtained by performing learning, as a class of the target data. An acquisition unit that acquires a corresponding tap coefficient; and a decoding unit that decodes the converted data into the original data by performing a predetermined prediction operation using the converted data and the tap coefficient corresponding to the class of the data of interest. It is characterized by having.

In the first data processing device, the decoding means can decode the converted data into the original data by performing a linear primary prediction operation using the converted data and the tap coefficients.

[0020] The first data processing device may further include storage means for storing tap coefficients for each class. In this case, the acquisition means includes a storage means for storing the tap coefficient corresponding to the class of the data of interest. Tap coefficients can be obtained.

In the first data processing device, the transformed data can be obtained by subjecting the original data to orthogonal transform or frequency transform and then quantizing.

The first data processing apparatus may further include an inverse quantization means for inversely quantizing the transformed data. In this case, the decoding means may use the inversely quantized transformed data for the original data. Data can be decrypted.

In the first data processing device, the additional information may be a quantization table used for quantizing original data.

In the first data processing device, the transformed data may be at least discrete cosine transformed from the original data.

The first data processing apparatus may further include a prediction tap extracting means for extracting the conversion data used together with the tap coefficient for predicting the data of interest and outputting the converted data as a prediction tap. Can perform a prediction operation using a prediction tap and a tap coefficient.

The first data processing apparatus further includes a class tap extracting means for extracting conversion data used to classify the target data into any of several classes and outputting the converted data as class taps. In this case, the class classification means can determine the class of the data of interest based on the additional information and the class tap.

In the first data processing device, the converted data is obtained by converting the original data into at least predetermined units.
The data may be orthogonally transformed or frequency transformed. In this case, the decoding means can decode the transformed data into the original data for each predetermined unit.

In the first data processing apparatus, the tap coefficient is such that the prediction error of the prediction value of the original data obtained by performing a predetermined prediction operation using the tap coefficient and the converted data is statistically minimized. Thus, it can be obtained by performing the learning.

In the first data processing device, the original data can be moving image or still image data.

According to a first data processing method of the present invention, a class classification of classifying target data of interest into one of several classes based on additional information. A step, an acquisition step of acquiring tap coefficients corresponding to the class of the data of interest, out of the tap coefficients for each predetermined class obtained by performing the learning, and taps corresponding to the class of the converted data and the data of interest. Decoding a converted data into original data by performing a predetermined prediction operation using the coefficient.

A first recording medium according to the present invention includes a class classification step of classifying target data of interest among original data into one of several classes based on additional information. And an acquisition step of acquiring a tap coefficient corresponding to the class of the data of interest among the tap coefficients for each predetermined class obtained by performing the learning, and a conversion coefficient for the tap coefficient corresponding to the class of the data of interest. And a decoding step of decoding the converted data into the original data by performing a predetermined prediction operation using the program.

The second data processing apparatus according to the present invention comprises: generating means for generating student data to be students by at least orthogonally or frequency-converting teacher data to be teachers; Classifying means for classifying the noted teacher data of interest into any of several classes based on the predetermined additional information used when generating the teacher data; Learning is performed so that the prediction error of the predicted value of the teacher data obtained by performing the prediction operation using the tap coefficient corresponding to the class and the student data is statistically minimized, and the tap coefficient for each class is obtained. Learning means.

In the second data processing apparatus, the learning means statistically minimizes the prediction error of the predicted value of the teacher data obtained by performing the linear primary prediction operation using the tap coefficients and the student data. Learning can be performed as follows.

In the second data processing apparatus, the generating means can generate student data by subjecting the teacher data to orthogonal transform or frequency transform and further quantizing it.

In the second data processing device, the additional information can be a quantization table used for quantizing the teacher data.

In the second data processing device, the generating means can generate student data by subjecting the teacher data to orthogonal transform or frequency transform, quantizing it, and then inversely quantizing it.

In the second data processing device, the generating means can generate the student data by at least performing discrete cosine transform on the teacher data.

The second data processing apparatus may further include a prediction tap extracting means for extracting student data used together with a tap coefficient for predicting the teacher data of interest and outputting it as a prediction tap. The means can perform learning so that the prediction error of the prediction value of the teacher data obtained by performing the prediction operation using the prediction tap and the tap coefficient is statistically minimized.

The second data processing device further includes class tap extracting means for extracting student data used for classifying the teacher data of interest into one of several classes and outputting the data as class taps. In this case, the class classification means can determine the class of the teacher data of interest based on the additional information and the class tap.

In the second data processing device, the generating means includes at least teacher data for each predetermined unit.
Student data can be generated by performing orthogonal transformation processing or frequency transformation.

In the second data processing device, the teacher data can be moving image or still image data.

According to a second data processing method of the present invention, at least orthogonal transformation or frequency transformation of teacher data to be a teacher is performed to generate student data to be a student. A classifying step of classifying the noted teacher data of interest into one of several classes based on the predetermined additional information used when generating the teacher data; Learning is performed so that the prediction error of the predicted value of the teacher data obtained by performing the prediction operation using the tap coefficient corresponding to the class and the student data is statistically minimized, and the tap coefficient for each class is obtained. And a learning step.

According to the second recording medium of the present invention, at least orthogonal transformation or frequency transformation of teacher data as a teacher is performed to generate student data as a student, and the student data is generated in the generating step. A classifying step of classifying the noted teacher data of interest into any of several classes based on the predetermined additional information used when performing the training; Learning so as to statistically minimize the prediction error of the predicted value of teacher data obtained by performing a prediction operation using the tap coefficient corresponding to the class and the student data, and obtaining the tap coefficient for each class And a program comprising steps.

In the first data processing apparatus, the data processing method, and the recording medium of the present invention, based on the additional information, the target data of interest among the original data is classified into several classes. The tap coefficients corresponding to the class of the data of interest among the tap coefficients for each of the predetermined classes, which are obtained by performing the learning and learning, are acquired. Then, by performing a predetermined prediction operation using the converted data and the tap coefficient corresponding to the class of the data of interest, the converted data is decoded into the original data.

In the second data processing apparatus, the data processing method, and the recording medium of the present invention, student data to be a student is generated by at least orthogonally or frequency-converting teacher data to be a teacher. Based on the predetermined additional information used when generating the student data, the focused teacher data of interest among the teacher data is classified into one of several classes. The learning is performed such that the prediction error of the predicted value of the teacher data obtained by performing the prediction operation using the tap coefficient and the student data corresponding to the class of the teacher data of interest is statistically minimized. The tap coefficient for each is obtained.

[0046]

FIG. 2 shows a configuration example of an embodiment of an image transmission system to which the present invention is applied.

The image data to be transmitted is
The encoder 21 encodes the image data supplied thereto, for example, by JPEG encoding to obtain encoded data. That is, the encoder 21 is configured, for example, in the same manner as the above-described JPEG encoding apparatus shown in FIG. 1A, and JPEG-encodes image data. Encoded data obtained by the encoder 21 performing JPEG encoding includes, for example, a semiconductor memory, a magneto-optical disk, a magnetic disk, an optical disk, a magnetic tape,
It is recorded on a recording medium 23 such as a phase change disk or the like.
(Cable Television) is transmitted via a transmission medium 24 such as a network, the Internet, or a public line.

The decoder 22 receives the encoded data provided via the recording medium 23 or the transmission medium 24,
Decode to original image data. The decoded image data is supplied to a monitor (not shown) and displayed, for example.

FIG. 3 shows an example of the configuration of the decoder 22 shown in FIG.

The encoded data is supplied to the entropy decoding circuit 3
1, the entropy decoding circuit 31 separates the encoded data into entropy-encoded quantized DCT coefficients and a quantization table as additional information added thereto. Further, the entropy decoding circuit 31 entropy-decodes the quantized DCT coefficient subjected to the entropy coding, and outputs the resulting quantized DCT coefficient Q for each block together with a quantization table as additional information to the coefficient conversion circuit 32. Supply.

The coefficient conversion circuit 32 uses the quantization table as the additional information as it is,
By performing a predetermined prediction operation using the CT coefficient Q and a tap coefficient obtained by performing learning described later, the quantized DCT coefficient for each block is decoded into an original block of 8 × 8 pixels. .

The block decomposition circuit 33 includes a coefficient conversion circuit 3
By deblocking the decoded block (decoded block) obtained in step 2, a decoded image is obtained and output.

Next, referring to the flowchart of FIG.
The processing of the decoder 22 in FIG. 3 will be described.

The encoded data is supplied to the entropy decoding circuit 3
1, the entropy decoding circuit 31 entropy-decodes the encoded data in step S1, and supplies the quantized DCT coefficient Q for each block to the coefficient transforming circuit 32. Also, the entropy decoding circuit 31
Separates the quantization table as additional information contained therein from the encoded data, and supplies it to the coefficient conversion circuit 32. In step S2, the coefficient conversion circuit 32 calculates the quantization D for each block from the entropy decoding circuit 31.
By performing a prediction operation using the quantization table and the tap coefficient, the CT coefficient Q is decoded into a pixel value for each block, and supplied to the block decomposition circuit 33. In step S3, the block decomposition circuit 33
Block decomposition for deblocking the block of pixel values from 2 (decoded block) is performed, and a decoded image obtained as a result is output, thus ending the processing.

Next, the coefficient conversion circuit 32 shown in FIG. 3 can decode the quantized DCT coefficients into pixel values by using, for example, the class classification adaptive processing.

The class classification adaptation process includes a class classification process and an adaptation process. The class classification process classifies the data into classes based on their properties, and performs the adaptation process for each class. Is based on the following method.

That is, in the adaptive processing, for example, the quantization DC
The quantized DCT coefficient is decoded into the original pixel value by obtaining the predicted value of the original pixel by a linear combination of the T coefficient and a predetermined tap coefficient.

More specifically, for example, a certain image is used as teacher data, and the image is subjected to DCT processing in block units and further quantized to obtain a quantized DCT.
The coefficient as student data, the prediction value of the original pixel value y is teacher data E [y], some of the quantized DCT coefficients x _1, x _2, a set of ..., predetermined tap coefficients w ₁ ,
Let us consider a case where a linear combination model defined by a linear combination of 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 +... (1)

To generalize equation (1), a matrix W consisting of a set of tap coefficients w _j , a matrix X consisting of a set of student data x _ij , and a matrix Y consisting of a set of predicted values E [y _j ] '
To

(Equation 1) Defines the following observation equation.

XW = Y ′ (2) Here, the component x _ij of the matrix X is a set of i-th student data (a set of student data used for predicting the _i-th teacher data y _i ). Means the j-th student data in the matrix W, and the component w _{j of the} matrix W represents a tap coefficient by which a product with the j-th student data in the set of the student data is calculated. Also, y
_i represents the i-th teacher data, and therefore, E [y _i ]
Represents the predicted value of the i-th teacher data. Note that y on the left side of the equation (1) is obtained by omitting the suffix i of the component y _i of the matrix Y. Further, x ₁ , x ₂ ,. The suffix i of the component x _ij is omitted.

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 original pixel value y. In this case, a true pixel value y serving as teacher data
And a predicted value E for a pixel value y
A matrix E consisting of a set of residuals e of [y] is

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

XW = Y + E (3)

In this case, the predicted value E close to the original pixel value y
The tap coefficient w _j for obtaining [y] is a square error

(Equation 3) Can be obtained by minimizing.

Therefore, the above square error is calculated by tap coefficient w _j
Is zero, that is, a tap coefficient w _j that satisfies the following equation is an optimum value for obtaining a predicted value E [y] close to the original pixel value y.

[0066]

(Equation 4) ... (4)

Therefore, first, the equation (3) is changed to the tap coefficient w.
By differentiating with _j , the following equation is established.

[0068]

(Equation 5) ... (5)

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

(Equation 6) ... (6)

Further, considering the relationship among the student data x _ij , the tap coefficient w _j , the teacher data y _i , and the residual e _{i in} the residual equation of the equation (3), the following equation is obtained from the equation (6). A normal equation can be obtained.

[0071]

(Equation 7) ... (7)

The normal equation shown in equation (7) is obtained by converting a matrix (covariance matrix) A and a vector v into

(Equation 8) If the vector W is defined as shown in Expression 1, it can be expressed by the following expression: AW = v (8)

Each normal equation in the equation (7) is prepared by preparing a certain number of sets of the student data x _ij and the teacher data y _i to form the same number as the number J of the tap coefficients w _{j to} be obtained. And therefore equation (8)
Is solved for the vector W (however, in order to solve the equation (8), the matrix A in the equation (8) needs to be regular) to obtain an optimal tap coefficient (here, the square error is minimized). Tap coefficient) w _j can be obtained. In addition,
In solving equation (8), for example, the sweeping method (Ga
uss-Jordan elimination method) can be used.

As described above, the optimum tap coefficient w _j
The adaptive processing is to obtain a predicted value E [y] close to the original pixel value y by using the tap coefficient _wj and using the equation (1).

For example, as teacher data, JPE
While using an image of the same image quality as the image to be G-coded,
When DCT and quantized DCT coefficients obtained by quantizing the teacher data are used as student data, a prediction error is used as a tap coefficient when decoding JPEG-coded image data into original image data. However, a statistically minimum one is obtained.

Therefore, even if the compression rate at the time of performing JPEG encoding is increased, that is, even if the quantization step used for quantization is made coarse, according to the adaptive processing, the prediction error is statistically minimized. Will be performed, and in effect,
The decoding process of the JPEG encoded image and the process of improving the image quality are performed at the same time.
As a result, even if the compression ratio is increased, the image quality of the decoded image can be maintained.

For example, as teacher data, JPE
An image having a higher image quality than the image to be G-encoded is used, and the image quality of the teacher data is used as student data according to JP.
The image quality is degraded to the same image quality as the image to be EG-coded.
When a quantized DCT coefficient obtained by CT and quantization is used, as a tap coefficient, when decoding JPEG-coded image data into high-quality image data, the prediction error is statistically minimized. Will be obtained.

Therefore, in this case, according to the adaptive processing, J
The decoding process of the PEG-encoded image and the process of further improving the image quality are performed at the same time. As described above, by changing the image quality of the image serving as the teacher data or the student data, it is possible to obtain a tap coefficient for setting the image quality of the decoded image to an arbitrary level.

FIG. 5 shows a first configuration example of the coefficient conversion circuit 32 shown in FIG. 3 for decoding quantized DCT coefficients into pixel values by the above-described class classification adaptive processing.

The quantized DCT coefficients for each block output from the entropy decoding circuit 31 (FIG. 3) are supplied to a prediction tap extraction circuit 41 and a class tap extraction circuit 42, and quantized as additional information. The table is supplied to the class classification circuit 43.

The prediction tap extracting circuit 41 supplies a block of quantized DCT coefficients supplied thereto (hereinafter referred to as DCT
A block having a pixel value corresponding to the block (hereinafter, referred to as a block) (hereinafter, referred to as a pixel block, as appropriate)
The target pixel block is sequentially set as a target pixel block, and each pixel constituting the target pixel block is set as a target pixel sequentially in a so-called raster scan order, for example. Further, the prediction tap extracting circuit 41 extracts a quantized DCT coefficient used for predicting the pixel value of the pixel of interest, and sets it as a prediction tap.

That is, as shown in FIG. 6, for example, as shown in FIG. 6, the prediction tap extraction circuit 41 calculates all quantized DCT coefficients of the DCT block corresponding to the pixel block to which the pixel of interest belongs, that is, 64 × 8 × 8 The optimized DCT coefficients are extracted as prediction taps. Therefore, in the present embodiment, the same prediction tap is formed for all pixels in a certain pixel block. However, the prediction tap can be configured with different quantized DCT coefficients for each target pixel.

The prediction taps for each pixel constituting the pixel block, that is, 64 sets of prediction taps for each of the 64 pixels, obtained in the prediction tap extraction circuit 41, are supplied to the product-sum operation circuit 45. However, in the present embodiment, as described above, since the same prediction tap is configured for all pixels of the pixel block, one set of prediction taps is actually set for one pixel block. It may be supplied to the product-sum operation circuit 45.

The class tap extracting circuit 42 extracts a quantized DCT coefficient used for class classification for classifying the pixel of interest into one of several classes, and sets it as a class tap.

In JPEG encoding, an image is encoded (DCT processing and quantization) for each pixel block. Therefore, all pixels belonging to a certain pixel block are classified into the same class, for example. And
Therefore, the class tap extracting circuit 42 forms the same class tap for each pixel of a certain pixel block. That is, as in the case of the prediction tap extraction circuit 41, for example, the class tap extraction circuit 42 generates the D corresponding to the pixel block to which the pixel of interest belongs as shown in FIG.
All 8 × 8 quantized DCT coefficients of the CT block are extracted as class taps.

Here, each pixel belonging to the pixel block is represented by
Classifying all pixel classes into the same class is equivalent to classifying the pixel block. Therefore, the class tap extracting circuit 42 configures one set of class taps for classifying the target pixel block, instead of 64 sets of class taps for classifying each of the 64 pixels constituting the target pixel block. Therefore, the class tap extraction circuit 42
Extracts, for each pixel block, 64 quantized DCT coefficients of a DCT block corresponding to the pixel block in order to classify the pixel block into a class tap.

The quantized DCT coefficients constituting the prediction taps and the class taps are not limited to the above-mentioned patterns.

The class tap of the pixel block of interest obtained by the class tap extracting circuit 42 is supplied to the class classifying circuit 43. The class classifying circuit 43 , The target pixel block is classified into classes based on the quantization table as additional information, and a class code corresponding to the class obtained as a result is output.

Here, as a method of classifying based on the class tap constituted by the quantized DCT coefficients,
For example, ADRC (Adaptive Dynamic Range Coding) or the like can be adopted.

In the method using ADRC, the quantized DCT coefficients constituting the class tap are subjected to ADRC processing, and the class of the target pixel block is determined according to the ADRC code obtained as a result.

In the K-bit ADRC, for example,
The maximum value MAX of the quantized DCT coefficient constituting the class tap
And the minimum value MIN is detected, and DR = MAX-MIN is set as the local dynamic range of the set.
, The quantized DCT coefficients making up the class tap are re-quantized to K bits. That is, from among the quantized DCT coefficients forming the class taps, the minimum value MIN is subtracted, and the subtracted value is divided (quantized) by DR / 2 ^K. Then, a bit string obtained by arranging the K-bit quantized DCT coefficients constituting the class tap in a predetermined order, which is obtained as described above, is output as an ADRC code. Therefore,
When the class tap is subjected to, for example, 1-bit ADRC processing, the quantized DCT coefficients constituting the class tap are obtained by subtracting the minimum value MIN from the maximum value MAX and the minimum value MI.
Divided by the average value with N, so that each quantized DCT
The coefficient is one bit (binarized). Then, a bit string in which the 1-bit quantized DCT coefficients are arranged in a predetermined order is output as an ADRC code.

The class classification circuit 43 includes, for example,
It is also possible to output the pattern of the level distribution of the quantized DCT coefficients constituting the class taps as it is as the class code. In this case, however, the class tap is constituted by N quantized DCT coefficients, and Assuming that K bits are assigned to the DCT coefficient, the number of class codes output from the classifying circuit 43 is (2
^N) becomes ^K Street, an enormous number exponentially proportional to the number of bits K of quantized DCT coefficients.

Therefore, in the classification circuit 43,
It is preferable to perform the class classification after compressing the information amount of the class tap by the above-described ADRC processing or vector quantization.

By the way, in the present embodiment, the class tap is composed of 64 quantized DCT coefficients as described above. Therefore, for example, even if class classification is performed by performing 1-bit ADRC processing on a class tap, the number of class codes has a large value of 2 ⁶⁴ .

Therefore, in the present embodiment, the classifying circuit 43 uses the quantized DCT constituting the class tap.
A feature amount having high importance is extracted from the coefficient, and the class is classified based on the feature amount, thereby reducing the number of classes.

That is, FIG. 7 shows the classification circuit 43 of FIG.
Is shown.

The class taps are supplied to the power calculation circuit 51. The power calculation circuit 51 divides the quantized DCT coefficients constituting the class taps into several spatial frequency bands, and Calculate the power in the frequency band.

That is, the power calculation circuit 51 converts the 8 × 8 quantized DCT coefficients constituting the class tap into, for example, four spatial frequency bands S ₀ , S ₁ , S ₂ , and S ₂ as shown in FIG.
Divided into S _3.

Here, each of the 8 × 8 quantized DCT coefficients constituting the class tap is represented by an alphabet x,
As shown in FIG. 6, assuming that the raster scan order is represented by adding sequential integers from 0, the spatial frequency band S ₀ has four quantized DCT coefficients x ₀ , x ₁ ,
consists x _8, x _9, the spatial frequency band S ₁ is 12 quantized DCT coefficients _{x 2, x 3, x 4} , x 5, x 6, x 7, x
_{10, x 11, x 12,} x 13, composed of x _14, x _15. Further, the spatial frequency band S ₂ is 12 quantized DCT coefficients _{x 16, x 17, x 24} , x 25, x 32, x 33, x 40, x 41, x
_48, is composed of _{x 49, x 56, x 57} , the spatial frequency band S
_3, 36 quantized DCT coefficients _{x 18, x 1 9, x} 20, x
_{21, x 22, x 23,} x 26, x 27, x 28, x 29, x 30,
_{x 31, x 34, x 3} 5, x 36, x 37, x 38, x 39, x 42, x
_{43, x 44, x 45,} x 46, x 47, x 50, x 5 1, x 52,
_{x 53, x 54, x 55} , x 58, x 59, x 60, x 61, x 62, x
Consists of ₆₃ .

Further, the power calculation circuit 51 performs quantization DC for each of the spatial frequency bands S ₀ , S ₁ , S ₂ , and S _3.
Calculate the power P ₀ , P ₁ , P ₂ , P ₃ of the AC component of the T coefficient,
Output to the class code generation circuit 52.

That is, the power operation circuit 51 operates in the spatial frequency band.
Area S₀For the above four quantized DCT coefficients x₀,
x₁, X₈, X₉AC component x of₁, X₈, X₉Sum of squares
x₁ ^Two+ X₈ ^Two+ X₉ ^TwoAnd calculate the power P₀As
Output to the Las code generation circuit 52. Also, the power calculation time
The road 51 corresponds to the above-mentioned 12 for the spatial frequency band S1.
AC components of the quantized DCT coefficients, ie, all 12
Is calculated, and the sum of squares of the quantized DCT coefficients of₁
To the class code generation circuit 52. Further
In addition, the power operation circuit 51 has a spatial frequency band S_TwoAnd S_ThreeNitsu
The spatial frequency band S₁As in
And each power P_TwoAnd P_ThreeAnd generate class code
Output to the circuit 52.

The class code generation circuit 52 converts the powers P ₀ , P ₁ , P ₂ , and P ₃ from the power calculation circuit 51 into the corresponding threshold values TH 0, TH stored in the threshold value table storage unit 53.
1, TH2, and TH3, respectively, and outputs a class code based on the magnitude relation. That is, the class code generation circuit 52 compares the power P ₀ and the threshold value TH0, obtain one-bit code representing the magnitude relationship. Similarly, the class code generation circuit 52 calculates the power P ₁ and the threshold T
H1, the power P ₂ and the threshold value TH2, the power P ₃ and the threshold value TH3,
By comparing each, a 1-bit code is obtained for each. Then, the class code generation circuit 5
2 is a 4-bit code obtained by arranging the four 1-bit codes obtained as described above in, for example, a predetermined order (accordingly, any value from 0 to 15). A class code representing the first class of the pixel block (hereinafter, appropriately referred to as a power class code) is used.

Further, the class code generation circuit 52 includes:
A quantization table as additional information is supplied, and the class code generation circuit 52 performs a class classification based on the additional information, thereby obtaining a class code representing the second class of the pixel block of interest. Get. That is, the class code generation circuit 52, for example,
In the EG encoding, if quantization is performed using any of the two types of quantization tables shown in FIG. 1B, the quantization table as additional information is It is determined which of the two types of quantization tables is used, and a 1-bit code representing the result of the determination is referred to as a class code representing the second class of the pixel block of interest (hereinafter referred to as an additional information class code as appropriate). ).

The class code generation circuit 52 converts the 4-bit power class code representing the first class into the second power class code.
By adding a 1-bit additional information class code representing the class of, a final class code for the pixel block of interest is generated and output. Therefore, in the present embodiment, the final class code is 5 bits, and the target pixel block is any one of the ²⁵ (= 32) classes (for example, any one of 0 to 31). (A class represented by a value).

Note that the method of classifying based on the quantization table is not limited to the method described above. That is, the class code generation circuit 52 prepares a plurality of quantization tables as standard patterns to be compared with a quantization table (quantization table actually used in JPEG encoding) supplied as additional information. ,
It is possible to detect a quantization table as a standard pattern closest to the quantization table as additional information, and output a code corresponding to the detection result as an additional information class code.

The threshold value table storage unit 53 stores threshold values TH0 to TH3 to be compared with the electric powers P _{0 to} P ₃ of the spatial frequency bands S _{0 to} S ₃ , respectively.

In the above case, the DC component x ₀ of the quantized DCT coefficient is not used in the class classification process, but the class classification process can also be performed using the DC component x _0. .

Returning to FIG. 5, the class code output from the classifying circuit 43 as described above is stored in the coefficient table storage unit 4.
4 is given as an address.

The coefficient table storage unit 44 stores a coefficient table in which tap coefficients for each class obtained by performing a learning process described later are registered.
The tap coefficient of the class stored at the address corresponding to the class code output from the class classification circuit 43 is output to the product-sum operation circuit 45.

Here, in the present embodiment, the pixel blocks are classified into classes.
One class code is obtained. On the other hand, in the present embodiment, since the pixel block is composed of 64 pixels of 8 × 8 pixels, 64 sets of tap coefficients for decoding each of the 64 pixels constituting the target pixel block are required. is there. Therefore, the coefficient table storage unit 44 stores, for an address corresponding to one class code,
64 sets of tap coefficients are stored.

The product-sum operation circuit 45 includes a prediction tap output from the prediction tap extraction circuit 41 and a coefficient table storage unit 44.
Is obtained, and the linear prediction operation (product-sum operation) shown in Expression (1) is performed using the prediction taps and the tap coefficients. The pixel value of the pixel is output to the block decomposition circuit 33 (FIG. 3) as a decoding result of the corresponding DCT block.

Here, in the prediction tap extraction circuit 41, as described above, each pixel of the target pixel block is sequentially set as the target pixel. Operation mode corresponding to the position of the pixel (hereinafter, appropriately referred to as pixel position mode)
And perform the processing.

That is, for example, among the pixels of the pixel block of interest, the i-th pixel in the raster scan order is represented as p _i, and when the pixel p _i is the pixel of interest, the product-sum operation circuit 45 , The processing of the pixel position mode #i is performed.

[0114] More specifically, as described above, the coefficient table storage unit 44 is to output the tap coefficients of the 64 sets for decoding the respective 64 pixels constituting the pixel block of interest, the pixel p _i of which If the set of tap coefficients for decoding denoted W _i, sum-of-products operation circuit 45, the operation mode is, when the pixel position mode #i includes a prediction tap, and a set W _i of the tap coefficients of the 64 sets Is used, and the result of the product-sum operation is used as the decoding result of the pixel p _i .

Next, referring to the flowchart of FIG.
The processing of the coefficient conversion circuit 32 in FIG. 5 will be described.

The quantized DCT coefficient for each block output from the entropy decoding circuit 31
1 and the class tap extraction circuit 42 sequentially receive the prediction block, and the prediction tap extraction circuit 41 sequentially sets the pixel blocks corresponding to the blocks of the quantized DCT coefficients (DCT blocks) supplied thereto as target pixel blocks.

Then, in step S11, the class tap extracting circuit 42 extracts, from the quantized DCT coefficients received there, one used for classifying the pixel block of interest, forms a class tap, and forms a class tap. This is supplied to the classification circuit 43.

The class classification circuit 43 is supplied with the class tap from the class classification circuit 42 and also with a quantization table as additional information output from the entropy decoding circuit 31. Circuit 43
In step S12, the class tap extraction circuit 4
Class tap from 2 and entropy decoding circuit 3
The target pixel block is classified into classes using the quantization table from 1 and the resulting class code is output to the coefficient table storage unit 44.

That is, in step S12, as shown in the flowchart of FIG.
, The power calculation circuit 51 of the class classification circuit 43 (FIG. 7) uses 8 × 8 quantized DCs forming a class tap.
The T coefficient is divided into four spatial frequency bands S _{0 to} S ₃ shown in FIG. 8, and respective powers P _{0 to} P ₃ are calculated.
The powers P _{0 to} P ₃ are output from the power calculation circuit 51 to the class code generation circuit 52.

The class code generation circuit 52 determines in step S
At 22, the threshold value TH0 is stored in the threshold value table storage unit 53.
To TH3 and read the power P from the power calculation circuit 51.
₀ to the P _3, respectively, compared with the threshold TH0 to TH3 respectively, to generate a power class codes based on the respective magnitude relationships.

Further, in step S23, the class code generation circuit 52 generates an additional information class code using a quantization table as additional information, and proceeds to step S24. In step S24, the class code generation circuit 52 generates a final class code from the power class code and the additional information class code, and returns.

Returning to FIG. 9, the class code obtained as described above in step S12 is given from the class classification circuit 43 to the coefficient table storage unit 44 as an address.

When the coefficient table storage unit 44 receives the class code as the address from the class classification circuit 43, it reads out the 64 sets of tap coefficients stored at that address in step S13, and sends it to the product-sum operation circuit 45. Output.

Then, the process proceeds to step S14, in which the prediction tap extracting circuit 41 sets, as a target pixel, a pixel which has not been set as the target pixel in the raster scan order among the pixels of the target pixel block, and sets the target pixel as a target pixel. A quantized DCT coefficient used for predicting a value is extracted and configured as a prediction tap. This prediction tap is a prediction tap extraction circuit 4
1 is supplied to the product-sum operation circuit 45.

Here, in the present embodiment, for each pixel block, all pixels in the pixel block are
Since the same prediction tap is configured, in practice, if the processing of step S14 is performed only on the pixel that is initially set as the target pixel for the target pixel block, the remaining 63
There is no need to do this for pixels.

In step S15, the product-sum operation circuit 45 acquires a set of tap coefficients corresponding to the pixel position mode for the target pixel from among the 64 sets of tap coefficients output from the coefficient table storage unit 44 in step S13. Using the set of tap coefficients and the prediction tap supplied from the prediction tap extraction circuit 41 in step S14, the product-sum operation shown in Expression (1) is performed to obtain a decoded value of the pixel value of the target pixel.

Then, the process proceeds to step S16, where the prediction tap extracting circuit 41 determines whether or not all pixels of the target pixel block have been processed as the target pixel.
If it is determined in step S16 that all the pixels of the target pixel block have not been processed yet as the target pixel, the process returns to step S14, and the prediction tap extraction circuit 41 returns the raster tap among the pixels of the target pixel block. In the scanning order, a pixel that has not been set as a target pixel is set as a new target pixel, and the same processing is repeated.

If it is determined in step S16 that all pixels of the target pixel block have been processed as the target pixel, that is, if the decoded values of all the pixels of the target pixel block have been obtained, The arithmetic circuit 45
Pixel block composed of the decoded value (decoded block)
Is output to the block decomposition circuit 33 (FIG. 3), and the process is terminated.

The process according to the flowchart of FIG. 9 is repeated each time the prediction tap extraction circuit 41 sets a new pixel block of interest.

Next, FIG. 11 shows an example of the configuration of an embodiment of a learning apparatus for performing a learning process of tap coefficients stored in the coefficient table storage section 44 of FIG.

One or more pieces of learning image data are supplied to the blocking circuit 61 as teacher data serving as a teacher at the time of learning.
Classifies an image as teacher data into 8 × 8 pixel blocks, as in the case of JPEG encoding.

The DCT circuit 62 sequentially reads out the pixel blocks formed by the blocking circuit 61 as a target pixel block, and performs a DCT process on the target pixel block to obtain a DCT coefficient block. This DC
The block of the T coefficient is supplied to the quantization circuit 63.

The quantization circuit 63 quantizes the block of DCT coefficients from the DCT circuit 62 in accordance with the same quantization table used for JPEG encoding, and obtains a block of quantized DCT coefficients (DCT coefficient) obtained as a result. Block) are sequentially supplied to the prediction tap extraction circuit 64 and the class tap extraction circuit 65.

That is, the quantization circuit 63 sets some of the general compression ratios used for JPEG encoding, quantizes the DCT coefficients according to the quantization table corresponding to each compression ratio, and sets the prediction taps. It is sequentially supplied to the extraction circuit 64 and the class tap extraction circuit 65. Further, the quantization circuit 63 supplies the quantization table used for quantization to the class classification circuit 66 as additional information. The same quantization table used by the quantization circuit 63 is stored as a standard pattern in the class code generation circuit 52 of FIG.

The prediction tap extracting circuit 64 selects, from the pixels of the target pixel block, the pixels which have not been set as the target pixel in the raster scan order as the target pixel, and sets the target pixel as the target tap extracting circuit shown in FIG. From the output of the quantization circuit 63, the same prediction tap
It is configured by extracting necessary quantized DCT coefficients. This prediction tap is sent from the prediction tap extraction circuit 64 to the normal equation addition circuit 6 as student data to be a student during learning.
7 is supplied.

The class tap extracting circuit 65 extracts, from the output of the quantizing circuit 63, the necessary quantized DCT coefficients for the target pixel block, using the same class taps as the class tap extracting circuit 42 shown in FIG. It is constituted by doing. This class tap is a class tap extraction circuit 65.
Is supplied to the class classification circuit 66.

The class classification circuit 66 performs the same processing as the class classification circuit 43 of FIG. 5 by using the class tap from the class tap extraction circuit 65 and the quantization table as additional information from the quantization circuit 63. By doing so, the target pixel block is classified into classes, and the resulting class code is supplied to the normal equation adding circuit 67.

The normal equation adding circuit 67 outputs the pixel value (of the pixel of interest) as teacher data from the blocking circuit 61.
And predictive taps (quantized DCT coefficients constituting the predictive taps) as student data from the predictive tap configuration circuit 64,
And addition for the pixel of interest.

That is, the normal equation adding circuit 67 uses the prediction taps (student data) for each class corresponding to the class code supplied from the class classification circuit 66 to generate each component in the matrix A of the equation (8). Multiplication (x _in x _im ) between the student data, and an operation corresponding to summation (Σ).

Further, the normal equation adding circuit 67 also uses the prediction tap (student data) and the pixel of interest (teacher data) for each class corresponding to the class code supplied from the class classification circuit 66 to obtain the equation (8). )), Multiplication (x _in y _i ) of student data and teacher data, which are components in the vector v, and an operation corresponding to summation (Σ) are performed.

The normal equation addition circuit 67
The above-described addition is performed for each class in each pixel position mode for the target pixel.

The normal equation adding circuit 67 performs the above-mentioned addition with all the pixels constituting the teacher image supplied to the blocking circuit 61 as target pixels, whereby for each class, for each pixel position mode, The normal equation shown in equation (8) is established.

The tap coefficient determination circuit 68 solves the normal equation generated for each class (and for each pixel position mode) in the normal equation addition circuit 67, thereby obtaining 64 sets of tap coefficients for each class. The coefficients are supplied to addresses corresponding to each class in the coefficient table storage unit 69.

Depending on the number of images prepared as learning images, the contents of the images, and the like, the normal equation adding circuit 67 may not be able to obtain the number of normal equations required for obtaining the tap coefficients. In such a case, the tap coefficient determination circuit 68 outputs, for example, a default tap coefficient for such a class.

The coefficient table storage unit 69 stores 64 sets of tap coefficients for each class supplied from the tap coefficient determination circuit 68.

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

The image data for learning is supplied to the blocking circuit 61 as teacher data.
In step S31, image data as teacher data is converted into 8 in the same manner as in JPEG encoding.
Step S:
Go to 32. In step S32, the DCT circuit 62
The pixel block divided by the blocking circuit 61 is
The target pixel block is sequentially read out and subjected to DCT processing to be a block of DCT coefficients.
Proceed to.

In step S33, the quantization circuit 63
One of the preset quantization tables that is not yet set as the target quantization table is
The value is set in the target quantization table and supplied to the class classification circuit 66, and the process proceeds to step S34. In step S34, the quantization circuit 63 sequentially reads out the blocks of the DCT coefficients obtained in the DCT circuit 62, quantizes them according to the target quantization table, and sets them as blocks (DCT blocks) composed of the quantized DCT coefficients. .

Then, the process proceeds to step S35, where the class tap extracting circuit 65 sets a pixel block which has not been set as a target pixel block among the pixel blocks divided by the blocking unit 61 as a target pixel block. further,
The class tap extracting circuit 65 extracts a quantized DCT coefficient used for classifying the pixel block of interest from the DCT block obtained by the quantizing circuit 63, forms a class tap, and supplies the class tap to the class classifying circuit 66. I do. In step S36, the class classification circuit 66 determines the class tap from the class tap extraction circuit 65 and the quantization circuit 63 in the same manner as described in the flowchart of FIG.
Is used to classify the pixel block of interest using the quantization table of interest, and the resulting class code is supplied to the normal equation adding circuit 67, and the flow advances to step S37.

In step S37, the prediction tap extracting circuit 64 sets a pixel which has not been set as the target pixel in the raster scan order among the pixels of the target pixel block as the target pixel, and sets the target pixel in FIG. The same prediction tap as that formed by the prediction tap extraction circuit 41 is configured by extracting necessary quantized DCT coefficients from the output of the quantization circuit 63. Then, the prediction tap extraction circuit 64
The prediction tap for the pixel of interest is used as student data.
The signal is supplied to the normal equation adding circuit 67, and the process proceeds to step S38.

In step S38, the normal equation adding circuit 67 reads the pixel of interest as teacher data from the blocking circuit 61, and predicts taps (quantized DCT coefficients constituting the student data) as student data and the teacher data as teacher data. The above-described addition of the matrix A and the vector v in Expression (8) is performed on the target pixel. This addition is performed for each class corresponding to the class code from the class classification circuit 66 and for each pixel position mode for the target pixel.

Then, proceeding to step S39, the prediction tap extracting circuit 64 determines whether or not all pixels of the target pixel block have been added as the target pixel. In step S39, when it is determined that all the pixels of the target pixel block have not been added as the target pixels, the process returns to step S37, and the prediction tap extracting circuit 64 determines, from among the pixels of the target pixel block, In the raster scan order, a pixel that has not been set as a target pixel is newly set as a target pixel, and the same processing is repeated thereafter.

If it is determined in step S39 that all the pixels of the target pixel block have been added as the target pixel, the process proceeds to step S40, where the blocking circuit 61 obtains the image from the image as the teacher data. It is determined whether or not all the obtained pixel blocks have been processed as the target pixel block. If it is determined in step S40 that all the pixel blocks obtained from the image as the teacher data have not been processed as the pixel block of interest, the process returns to step S35.
Of the pixel blocks that have been blocked by the blocking circuit 61, those that have not yet been set as the target pixel block are newly set as the target pixel block, and the same processing is repeated thereafter.

On the other hand, in step S40, all pixel blocks obtained from the image as teacher data are
When it is determined that the processing has been performed as the target pixel block, the process proceeds to step S41, and the quantization circuit 63 determines whether all of the preset quantization tables have been processed as the target quantization table. If it is determined in step S41 that all of the preset quantization tables have not been processed as the target quantization table, the process returns to step S33, and the process returns to step S33 for all learning image data.
Subsequent processing is repeated.

If it is determined in step S41 that all of the preset quantization tables have been processed as the target quantization table, the process proceeds to step S41.
Proceeding to 42, the tap coefficient determination circuit 68 solves the normal equation generated for each class of pixel position mode in the normal equation addition circuit 67, and for each class, The corresponding 64 sets of tap coefficients are obtained, supplied to the addresses corresponding to each class in the coefficient table storage section 69 and stored, and the process is terminated.

As described above, the coefficient table storage unit 6
The tap coefficients for each class stored in 9 are stored in the coefficient table storage unit 44 in FIG.

Therefore, the tap coefficients stored in the coefficient table storage unit 44 are such that the prediction error (here, the square error) of the prediction value of the original pixel value obtained by performing the linear prediction operation is statistically minimized. The coefficient conversion circuit 32 shown in FIG. 5 decodes a JPEG-coded image into an image that is as close as possible to the original image. Can be.

As described above, the decoding processing of the JPEG-coded image and the processing for improving the image quality are simultaneously performed, so that the JPEG-coded image can be efficiently processed. Thus, a decoded image with good image quality can be obtained.

In the present embodiment, the class classification in the class classification circuit 43 (66) is performed not only by using the class tap but also by using a quantization table as additional information. This can be done using only taps. However, as mentioned above,
By performing the class classification using the quantization table as the additional information, fine classification can be performed, so to speak, and the image quality of the decoded image can be further improved.

Next, FIG. 13 shows the coefficient conversion circuit 32 of FIG.
2 shows a second configuration example. In the figure, portions corresponding to those in FIG. 5 are denoted by the same reference numerals, and a description thereof will be omitted as appropriate below. That is, the coefficient conversion circuit 32 in FIG. 13 has basically the same configuration as that in FIG. 5 except that an inverse quantization circuit 71 is newly provided.

In the embodiment shown in FIG. 13, the inverse quantization circuit 71 is supplied with quantized DCT coefficients for each block obtained by entropy decoding the encoded data in the entropy decoding circuit 31 (FIG. 3). . Further, the inverse quantization circuit 71 includes an entropy decoding circuit 3
A quantization table as additional information output by 1 is also supplied.

The inverse quantization circuit 71 inversely quantizes the quantized DCT coefficient from the entropy decoding circuit 31 in accordance with the quantization table from the entropy decoding circuit 31, and converts the resulting DCT coefficient into a prediction tap extraction circuit. 41 and a class tap extraction circuit 42.

Therefore, in the prediction tap extraction circuit 41 and the class tap extraction circuit, not the quantized DCT coefficient but
A prediction tap and a class tap are respectively configured for DCT coefficients, and thereafter, for DCT coefficients,
The same processing as in FIG. 5 is performed.

As described above, in the embodiment of FIG. 13, the processing is performed not on the quantized DCT coefficient but on the DCT coefficient, so that the tap coefficients stored in the coefficient table storage unit 44 are the same as those in FIG. It needs to be different.

FIG. 14 shows an example of the configuration of an embodiment of a learning device for performing a learning process of tap coefficients stored in the coefficient table storage section 44 of FIG. In the figure, portions corresponding to the case in FIG. 11 are denoted by the same reference numerals, and the description thereof will be appropriately omitted below. That is, the learning device in FIG. 14 is configured basically in the same manner as in FIG. 11 except that an inverse quantization circuit 81 is newly provided at the subsequent stage of the quantization circuit 63.

In the embodiment shown in FIG. 14, the inverse quantization circuit 81 has a quantized DCT output from the inverse quantization circuit 63.
A coefficient and a quantization table as additional information are supplied. Then, the inverse quantization circuit 81 inversely quantizes the quantized DCT coefficient from the inverse quantization circuit 63 in accordance with the quantization table from the inverse quantization circuit 63, and extracts the DCT coefficient obtained as a result of prediction tap extraction. It is supplied to the circuit 64 and the class tap extraction circuit 65.

Therefore, in the prediction tap extraction circuit 64 and the class tap extraction circuit 65, not the quantized DCT coefficient but
A prediction tap and a class tap are respectively configured for DCT coefficients, and thereafter, for DCT coefficients,
The same processing as in the case of FIG. 11 is performed.

As a result, a tap coefficient that reduces the influence of a quantization error caused by the DCT coefficient being quantized and further dequantized is obtained.

Next, FIG. 15 shows the coefficient conversion circuit 32 of FIG.
3 shows a third configuration example. In the figure, portions corresponding to those in FIG. 5 are denoted by the same reference numerals, and a description thereof will be omitted as appropriate below. That is, the coefficient conversion circuit 32 of FIG.
Is basically the same as in FIG.

Therefore, in the embodiment shown in FIG. 15, the class classification circuit 43 performs the class classification based only on the quantization table as additional information supplied thereto, and converts the resulting additional information class code into The final class code is supplied to the coefficient table storage unit 44 as it is.

In the present embodiment, the additional information class code is one bit, as described above.
In the coefficient table storage unit 44, only tap coefficients of 2 (= 2 ¹ ) classes are stored, and processing is performed using the tap coefficients.

As described above, in the embodiment of FIG. 15, the tap coefficients stored in the coefficient table storage section 44 are:
It is different from the case in FIG.

FIG. 16 shows an example of the configuration of an embodiment of the learning apparatus for performing the learning process of the tap coefficients stored in the coefficient table storage section 44 of FIG. In the figure, portions corresponding to the case in FIG. 11 are denoted by the same reference numerals, and the description thereof will be appropriately omitted below. That is, the learning device in FIG. 16 is configured basically in the same manner as in FIG. 11 except that the class tap extraction circuit 65 is not provided.

Therefore, in the learning apparatus of FIG. 16, the above-described addition is performed for each class obtained based on only the additional information in the normal equation adding circuit 67. Then, in the tap coefficient determination circuit 68, by solving the normal equation generated by such addition,
A tap coefficient is determined.

Next, FIG. 17 shows the coefficient conversion circuit 32 of FIG.
4 shows a fourth configuration example. Note that, in the drawing, the same reference numerals are given to portions corresponding to the case in FIG. 5 or FIG. 13, and the description thereof will be appropriately omitted below. That is, the coefficient conversion circuit 32 in FIG. 17 does not include the class tap extraction circuit 42, and
The configuration is basically the same as that in FIG.

Therefore, in the embodiment of FIG. 17, FIG.
Similarly to the case of the embodiment, the coefficient table storage unit 44 stores only the tap coefficients of the classes obtained by the class classification performed only based on the quantization table as the additional information. The processing is performed.

Further, in the embodiment of FIG.
Similarly to the case of the embodiment, in the prediction tap extraction circuit 41, prediction taps are configured not for the quantized DCT coefficients but for the DCT coefficients output from the inverse quantization circuit 71. Processing is performed as an object.

Therefore, also in the embodiment of FIG. 17, the tap coefficients stored in the coefficient table storage section 44 are the same as those in FIG.
It is different from the case of.

FIG. 18 shows an example of the configuration of an embodiment of the learning apparatus for performing the learning process of the tap coefficients stored in the coefficient table storage section 44 of FIG. In the drawings, parts corresponding to those in FIG. 11 or FIG. 14 are denoted by the same reference numerals, and a description thereof will be appropriately omitted below. That is, the learning device in FIG. 18 is configured basically in the same manner as in FIG. 11 except that the class tap extraction circuit 65 is not provided and the inverse quantization circuit 81 is newly provided. I have.

Therefore, in the learning apparatus of FIG. 18, the prediction tap extracting circuit 64 uses not the quantized DCT coefficient but
Prediction taps are configured for DCT coefficients, and thereafter, processing is performed for DCT coefficients. further,
In the normal equation adding circuit 67, the above addition is
The tap coefficient is obtained for each class obtained by the class classification performed based only on the quantization table as the additional information, and the tap coefficient is obtained by solving such a normal equation for each class in the tap coefficient determination circuit 68.

Next, in the above description, JPEG-encoded images for compressing and encoding still images are used. However, the present invention relates to compression-encoding of moving images, for example, for MPEG-encoded images. It is also possible to use

That is, FIG. 19 shows an example of the configuration of the encoder 21 shown in FIG. 2 when MPEG coding is performed.

The frames (or fields) constituting the moving image to be MPEG-encoded are sequentially supplied to a motion detection circuit 91 and a computing unit 92.

The motion detection circuit 91 detects a motion vector of the frame supplied thereto in units of macro blocks, and supplies the motion vector to the entropy coding circuit 96 and the motion compensation circuit 100.

The computing unit 92 determines that the image supplied thereto is
If the picture is an I (Intra) picture, it is supplied to the blocking circuit 93 as it is, and the picture is P (Predictive) or B (Bidirectional).
ly predictive) picture, the motion compensation circuit 100
, And the difference value is supplied to the blocking circuit 93.

The blocking circuit 93 blocks the output of the computing unit 92 into 8 × 8 pixel blocks,
The signal is supplied to a circuit 94. The DCT circuit 94 performs a DCT process on the pixel block from the blocking circuit 93, and supplies a DCT coefficient obtained as a result to the quantization circuit 95. The quantization circuit 95 receives the D from the DCT circuit 93 in block units.
The CT coefficients are quantized according to a predetermined quantization table, and the resulting quantized DCT coefficients are supplied to the entropy coding circuit 96 together with the used quantization table. The entropy encoding circuit 96 includes the quantization circuit 9
5 is entropy-encoded, and the motion vector from the motion detection circuit 91 and the quantization circuit 95
, And other necessary information, and the resulting encoded data is output as an MPEG encoding result.

Of the quantized DCT coefficients output from the quantization circuit 95, I-pictures and P-pictures need to be locally decoded to be used as reference pictures of P-pictures and B-pictures to be encoded later. In addition to the entropy encoding circuit 96, the signal is also supplied to an inverse quantization circuit 97. Further, the quantization table used in the quantization circuit 95 is also supplied to the inverse quantization circuit 97.

The inverse quantization circuit 97 inversely quantizes the quantized DCT coefficient from the quantization circuit 95 in accordance with the quantization table from the quantization circuit 95, thereby obtaining D
It is supplied to the inverse DCT circuit 98 as a CT coefficient. Inverse DCT
The circuit 98 converts the DCT coefficient from the inverse quantization circuit 97 into an inverse D
CT processing is performed, and the result is output to the arithmetic unit 99. In the arithmetic unit 99,
In addition to the output of the inverse DCT circuit 98, a reference image output by the motion compensation circuit 100 is also supplied. The arithmetic unit 99 outputs the output signal of the inverse DCT circuit 98 when the output of the inverse DCT circuit 98 is a P-picture. , And the output of the motion compensation circuit 100, the original image is decoded, and the result is supplied to the motion compensation circuit 100. When the output of the inverse DCT circuit 98 is that of an I picture, the arithmetic unit 99 supplies the output to the motion compensation circuit 100 as it is because the output is a decoded image of the I picture.

The motion compensation circuit 100 performs motion compensation on the locally decoded image supplied from the computing unit 99 according to the motion vector from the motion detection circuit 91, and outputs the image after the motion compensation. The reference images are supplied to computing units 92 and 99.

Here, FIG. 20 shows the above-described MPEG format.
1 shows an example of a configuration of a conventional MPEG decoder for decoding encoded data obtained as a result of encoding.

The encoded data is supplied to the entropy decoding circuit 1
The entropy decoding circuit 111 entropy-decodes the encoded data to obtain a quantized DCT coefficient, and separates a motion vector, a quantization table, and other necessary information included in the encoded data.
Then, the quantized DCT coefficient and the quantization table are supplied to the inverse quantization circuit 112, and the motion vector is supplied to the motion compensation circuit 116.

The inverse quantization circuit 112 inversely quantizes the quantized DCT coefficient from the entropy decoding circuit 111 in accordance with the quantization table from the entropy decoding circuit 11 to obtain a DCT coefficient. Supply. The inverse DCT circuit 113 performs an inverse DCT process on the DCT coefficient from the inverse quantization circuit 112 and
14 is output. The arithmetic unit 114 includes the inverse quantization circuit 11
In addition to the output of (3), an I-picture or P-picture already decoded, which has been output from the motion compensation circuit 116 and subjected to motion compensation according to the motion vector from the entropy decoding circuit 111, is supplied as a reference image. The operation unit 114 outputs the signal from the inverse DCT circuit 113 to P
Or if it is a B picture, its output,
By adding the output of the motion compensation circuit 116, the original image is decoded and supplied to the block decomposition circuit 115. The arithmetic unit 114 outputs the output of the inverse DCT circuit 113
In the case of a picture, the output is a decoded picture of an I-picture, so that it is supplied to the block decomposition circuit 115 as it is.

The block disassembly circuit 115 includes an arithmetic unit 114
By deblocking the decoded image supplied in pixel block units from, a decoded image is obtained and output.

On the other hand, the motion compensation circuit 116
4 receives the I-picture and the P-picture of the decoded image, and performs motion compensation according to the motion vector from the entropy decoding circuit 111. Then, the motion compensation circuit 116 supplies the image after the motion compensation to the arithmetic unit 114 as a reference image.

According to the decoder 22 shown in FIG. 3, the encoded data which has been MPEG-encoded can be efficiently processed as described above.
The image can be decoded into a high-quality image.

That is, the coded data is supplied to an entropy decoding circuit 31, which entropy decodes the coded data to obtain a quantized DCT coefficient, and obtains a motion vector included in the coded data. Separate quantization tables and other necessary information.
Then, the quantized DCT coefficient is output to the entropy decoding circuit 3
1 to the coefficient conversion circuit 32, and the quantization table, the motion vector, and the like are also supplied as additional information from the entropy decoding circuit 31 to the coefficient conversion circuit 32.

The coefficient conversion circuit 32 performs a predetermined prediction operation using the quantized DCT coefficient Q from the entropy decoding circuit 31, the additional information, and the tap coefficient obtained by performing the learning, and performs an entropy decoding circuit. 3
By performing motion compensation according to the motion vector from 1 as needed, the quantized DCT coefficient is decoded into the original pixel value and supplied to the block decomposition circuit 33.

The block decomposition circuit 33 includes the coefficient conversion circuit 3
The decoded image is obtained and output by deblocking the pixel block composed of the decoded pixels obtained in 2.

Next, FIG.
4 shows a configuration example of the coefficient conversion circuit 32 in FIG. 3 when decoding PEG-encoded data. Note that, in the figure, the same reference numerals are given to portions corresponding to the case in FIG. 17 or FIG. 20, and the description thereof will be appropriately omitted below. That is, the coefficient conversion circuit 32 shown in FIG.
Is configured in the same manner as in FIG. 17 except that the arithmetic unit 114 and the motion compensation circuit 116 in FIG. 20 are provided at the subsequent stage of the product-sum operation circuit 45.

Therefore, in the coefficient conversion circuit 32 of FIG.
The same processing as in the case of FIG.
This is performed in place of the inverse DCT processing in the inverse DCT circuit 113 of the decoder, and thereafter, a decoded image is obtained in the same manner as in FIG.

In the embodiment shown in FIG. 21, the additional information supplied to the classifying circuit 43 includes a motion vector in addition to the quantization table. Classification can be performed based on a motion vector in addition to the table. In the class classification based on the motion vector, for example, a code indicating the magnitude relationship between the magnitude of the motion vector and a predetermined threshold,
A code or the like representing a magnitude relationship between each of the x component and the y component of the motion vector and a predetermined threshold value can be used as a class code.

Next, FIG. 22 shows an example of the configuration of an embodiment of a learning device for learning tap coefficients stored in the coefficient table storage section 44 of FIG. In FIG. 1, FIG.
8 are denoted by the same reference numerals, and the description thereof will be appropriately omitted below.

A learning image is input to the motion vector detection circuit 121 and the arithmetic unit 122 as teacher data. Motion vector detection circuit 121, arithmetic unit 122, blocking circuit 123, DCT circuit 124, quantization circuit 1
25, the inverse quantization circuit 127, the inverse DCT circuit 128, the computing unit 129, or the motion compensation circuit 130 are the motion vector detecting circuit 91, the computing unit 92, and the blocking circuit 9 in FIG.
3. DCT circuit 94, quantization circuit 95, inverse quantization circuit 9
7, the same processing as that performed by the inverse DCT circuit 98, the arithmetic unit 99, or the motion compensation circuit 100, whereby the quantization circuit 125 performs the same quantization as that output by the quantization circuit 95 in FIG. The DCT coefficient and the quantization table are output.

The quantized DCT output from the quantization circuit 125
The coefficients and the quantization table are supplied to an inverse quantization circuit 81, which inversely quantizes the quantized DCT coefficients from the quantization circuit 125 in accordance with a quantization step from the quantization circuit 125. , DCT coefficients, and supplies them to the prediction tap extraction circuit 64. The prediction tap extraction circuit 64 forms a prediction tap from the DCT coefficient from the inverse quantization circuit 81 and supplies the prediction tap to the normal equation addition circuit 67.

On the other hand, the class classification circuit 66 classifies based on the quantization table output from the quantization circuit 125. When the class classification circuit 43 in FIG. 21 performs the class classification based on the quantization table and the motion vector, the class classification circuit 66
And the motion vector detection circuit 1
Classification is performed on the basis of the motion vector output by 21.

The class code obtained as a result of the class classification by the class classification circuit 66 is supplied to a normal equation addition circuit 67. The normal equation addition circuit 67 uses the output of the arithmetic unit 122 as teacher data and an inverse quantization circuit. Using the prediction tap from 81 as student data, the above-described addition is performed for each class, thereby generating a normal equation.

Then, the tap coefficient determination circuit 68 solves the normal equation for each class generated by the normal equation addition circuit 67 to obtain the tap coefficient for each class.
The data is supplied to the coefficient table storage unit 69 and stored.

The product-sum operation circuit 45 shown in FIG. 21 uses the tap coefficients for each class obtained in this way to calculate M
Since the PEG-encoded data is decoded, the decoding process of the MPEG-encoded image and the process for improving the image quality can be performed simultaneously, and therefore, the MPEG-encoded image can be decoded. Thus, a decoded image with good image quality can be efficiently obtained from the decoded image.

The coefficient conversion circuit 32 shown in FIG. 21 can be configured without providing the inverse quantization circuit 71. In this case, the learning device in FIG. 22 may be configured without the inverse quantization circuit 81.

The coefficient conversion circuit 32 shown in FIG.
As in the case of, it is possible to provide and configure the class tap extraction circuit. In this case, the learning device in FIG. 22 may be provided with the class tap extraction circuit 65 as in the case in FIG.

[0211] In the above case, the quantization table and the motion vector are used as the additional information. However, as the additional information, various other information not necessarily required to restore the DCT coefficients to the original state. Can be adopted. That is, for example, regarding encoded data obtained by MPEG encoding, as the additional information, a picture type, a macro block type, and the like can be employed in addition to a quantization table and a motion vector.

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

FIG. 23 shows an example of the configuration of an embodiment of a computer in which a program for executing the above-described series of processing is installed.

The program is stored in a hard disk 205 or a ROM 2 as a recording medium built in the computer.
03 can be recorded in advance.

Alternatively, the program may be a floppy (registered trademark) disk, CD-ROM (Compact Disc Read Onl
y Memory), MO (Magneto optical) disc, DVD (Digita
l Versatile Disc), a magnetic disk, a semiconductor memory, etc., can be temporarily or permanently stored (recorded) in a removable recording medium 211. Such a removable recording medium 211 can be provided as so-called package software.

[0216] The program is installed in the computer from the removable recording medium 211 as described above, and transmitted from a download site to the computer wirelessly via an artificial satellite for digital satellite broadcasting, or transmitted to a LAN (Local Area Area Network). Network) or the Internet, and the program can be transferred to the computer by wire, and the computer can receive the transferred program by the communication unit 208 and install the program on the built-in hard disk 205.

The computer has a CPU (Central Processing).
Unit 202. The CPU 202 has a bus 2
01, the input / output interface 210 is connected. The CPU 202 operates the input unit 207 including a keyboard, a mouse, a microphone, and the like by the user via the input / output interface 210. When a command is input, the ROM (Read O
(Nly Memory) 203 is executed. Alternatively, the CPU 202 may execute a program stored in the hard disk 205, a program transferred from a satellite or a network, received by the communication unit 208 and installed on the hard disk 205, or a removable recording medium 211 mounted on the drive 209. The program read and installed on the hard disk 205 is stored in a RAM (Random Access Memory).
y) Load to 204 and execute. This allows the CPU 20
2 performs processing according to the above-described flowchart or processing performed by the configuration of the above-described block diagram. Then, the CPU 202 transmits the processing result as necessary, for example, via the input / output interface 210.
An output is made from an output unit 206 including an LCD (Liquid CryStal Display), a speaker, or the like, or transmitted from a communication unit 208, and further recorded on the hard disk 205.

Here, in the present specification, processing steps for writing a program for causing a computer to perform various types of processing do not necessarily have to be processed in chronological order in the order described in the flowchart, and may be performed in parallel. Alternatively, it also includes processing executed individually (for example, parallel processing or processing by an object).

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

Although the present embodiment is directed to image data, the present invention is also applicable to, for example, audio data.

In this embodiment, at least D
Although JPEG encoding for performing CT processing and decoding of MPEG-encoded data are performed, the present invention is applicable to decoding of data transformed by other orthogonal transformation or frequency transformation. That is, the present invention is also applicable to, for example, decoding subband encoded data or Fourier transformed data.

Further, in this embodiment, the decoder 22
In, the tap coefficients used for decoding are stored in advance, but the tap coefficients can be included in the encoded data and provided to the decoder 22.

In this embodiment, the decoding is performed by the linear primary prediction operation using the tap coefficients. However, the decoding can also be performed by the second or higher order prediction operation. It is.

[0224]

According to the first data processing apparatus, the data processing method, and the recording medium of the present invention, based on the additional information, the target data of interest out of the original data is converted into several data. The tap coefficients corresponding to the class of the data of interest are acquired from the tap coefficients for each of the predetermined classes, which are classified into any of the classes and obtained by performing the learning. Then, by performing a predetermined prediction operation using the converted data and the tap coefficient corresponding to the class of the data of interest, the converted data is decoded into the original data. Therefore, the converted data can be efficiently decoded.

According to the second data processing apparatus, the data processing method, and the recording medium of the present invention, student data to be a student is generated by at least orthogonally or frequency-transforming teacher data to be a teacher. On the basis of the predetermined additional information used when generating the student data, the noted teacher data of interest out of the teacher data is classified into one of several classes. The learning is performed such that the prediction error of the predicted value of the teacher data obtained by performing the prediction operation using the tap coefficient and the student data corresponding to the class of the teacher data of interest is statistically minimized. The tap coefficient for each is obtained. Therefore, by using the tap coefficient, the orthogonally transformed or frequency transformed data is
Decoding can be performed efficiently.

[Brief description of the drawings]

FIG. 1 is a diagram for explaining conventional JPEG encoding / decoding.

FIG. 2 is a diagram illustrating a configuration example of an embodiment of an image transmission system to which the present invention has been applied.

FIG. 3 is a block diagram illustrating a configuration example of a decoder 22 in FIG. 2;

FIG. 4 is a flowchart illustrating a process of a decoder 22 in FIG. 3;

FIG. 5 is a block diagram illustrating a first configuration example of a coefficient conversion circuit 32 in FIG. 3;

FIG. 6 is a diagram illustrating an example of a prediction tap and a class tap.

FIG. 7 is a block diagram illustrating a configuration example of a class classification circuit 43 in FIG. 5;

FIG. 8 is a diagram for explaining processing of a power calculation circuit 51 of FIG. 5;

FIG. 9 is a flowchart illustrating a process of a coefficient conversion circuit 32 of FIG. 5;

FIG. 10 is a flowchart illustrating details of a process in step S12 of FIG. 9;

FIG. 11 is a block diagram illustrating a configuration example of a first embodiment of a learning device to which the present invention has been applied.

FIG. 12 is a flowchart illustrating a process of the learning device in FIG. 11;

FIG. 13 is a block diagram illustrating a second configuration example of the coefficient conversion circuit 32 of FIG. 3;

FIG. 14 is a block diagram illustrating a configuration example of a second embodiment of a learning device to which the present invention has been applied.

FIG. 15 is a block diagram showing a third configuration example of the coefficient conversion circuit 32 of FIG. 3;

FIG. 16 is a block diagram illustrating a configuration example of a third embodiment of a learning device to which the present invention has been applied.

FIG. 17 is a block diagram illustrating a fourth configuration example of the coefficient conversion circuit 32 of FIG. 3;

FIG. 18 is a block diagram illustrating a configuration example of a fourth embodiment of a learning device to which the present invention has been applied.

19 is a block diagram illustrating a configuration example of an encoder 21 in FIG.

FIG. 20 is a block diagram illustrating a configuration example of an MPEG decoder.

21 is a block diagram illustrating a fifth configuration example of the coefficient conversion circuit 32 in FIG. 3;

FIG. 22 is a block diagram illustrating a configuration example of a fifth embodiment of a learning device to which the present invention has been applied.

FIG. 23 is a block diagram illustrating a configuration example of a computer according to an embodiment of the present invention.

[Description of Signs] 21 encoder, 22 decoder, 23 recording medium, 24 transmission medium, 31 entropy decoding circuit, 32 coefficient conversion circuit, 33 block decomposition circuit, 41 prediction tap extraction circuit, 42 class tap extraction circuit, 43 class classification circuit , 44 coefficient table storage unit, 45 product-sum operation circuit, 51 power operation circuit, 52 class code generation circuit, 53 threshold table storage unit, 61 blocking circuit, 62 DCT
Circuit, 63 quantization circuit, 64 prediction tap extraction circuit, 65 class tap extraction circuit, 66 class classification circuit, 67 normal equation addition circuit, 68 tap coefficient determination circuit, 69 coefficient table storage section, 71,
81 inverse quantization circuit, 114 operation unit, 115 motion compensation circuit, 121 motion vector detection circuit, 122
Arithmetic unit, 123 block circuit, 124 DCT
Circuit, 125 quantization circuit, 127 inverse quantization circuit,
128 inverse DCT circuit, 129 arithmetic unit, 130
Motion compensation circuit, 201 bus, 202 CPU,
203 ROM, 204 RAM, 205 hard disk, 206 output unit, 207 input unit, 208
Communication unit, 209 drive, 210 input / output interface, 211 removable recording medium

 ────────────────────────────────────────────────── ─── Continued on the front page (72) Hideo Nakaya, Inventor 6-7-35 Kita-Shinagawa, Shinagawa-ku, Tokyo Inside Sony Corporation (72) Inventor Takeharu Nishikata 6-35, Kita-Shinagawa, Shinagawa-ku, Tokyo Inside Sony Corporation (72) Inventor Hideki Otsuka 6-7-35 Kita-Shinagawa, Shinagawa-ku, Tokyo Sony Corporation (72) Inventor Takeshi Kunihiro 6-35 Kita-Shinagawa, Shinagawa-ku, Tokyo Soni (Inc.) (72) Inventor Takafumi Morito 6-7-35 Kita-Shinagawa, Shinagawa-ku, Tokyo Sony Corporation (72) Inventor Masashi Uchida 6-35, Kita-Shinagawa, Shinagawa-ku, Tokyo Sony Corporation Company F term (reference) 5C059 KK01 MA00 MA23 MC14 NN01 PP01 PP04 PP05 PP06 PP07 SS06 SS11 SS20 TA48 TB08 TC03 TC12 TD13 UA05 5C078 AA04 BA32 BA57 CA00 DA02

Claims (26)

[Claims] 1. A data process for processing encoded data including at least transform data obtained by performing an orthogonal transform process or a frequency transform process and predetermined additional information, and decoding the transform data into original data An apparatus, comprising: classifying means for classifying, based on the additional information, data of interest of the original data into one of several classes; and learning. Obtaining means for obtaining a tap coefficient corresponding to the class of the data of interest, among the tap coefficients for each predetermined class obtained by And a decoding means for decoding the converted data into original data by performing a predetermined prediction operation. . 2. The apparatus according to claim 1, wherein said decoding means decodes said converted data into original data by performing a linear primary prediction operation using said converted data and tap coefficients. The data processing device according to claim 1. 3. A storage unit for storing tap coefficients for each class, wherein the obtaining unit obtains a tap coefficient corresponding to the class of the target data from the storage unit. The data processing device according to claim 1. 4. The conversion data is obtained by converting the original data into
2. The data processing device according to claim 1, wherein the data is obtained by performing orthogonal transformation or frequency transformation and further performing quantization. 5. The image processing apparatus according to claim 1, further comprising: an inverse quantization unit that inversely quantizes the transformed data, wherein the decoding unit decodes the inversely quantized transformed data into the original data. Item 5. The data processing device according to Item 4. 6. The data processing apparatus according to claim 4, wherein the additional information is a quantization table used for quantizing the original data. 7. The conversion data is obtained by converting the original data into
2. The data processing apparatus according to claim 1, wherein the data processing apparatus performs at least discrete cosine transform. 8. The apparatus according to claim 1, further comprising: a prediction tap extracting unit that extracts the transformed data used together with the tap coefficient to predict the target data and outputs the converted data as a prediction tap. 2. The data processing apparatus according to claim 1, wherein the prediction operation is performed using the data. 9. A class tap extracting means for extracting the conversion data used for classifying the data of interest into one of several classes, and outputting the converted data as a class tap, 2. The data processing device according to claim 1, wherein a class of the target data is obtained based on the additional information and a class tap. 10. The conversion data is obtained by subjecting the original data to at least orthogonal transform processing or frequency conversion for each predetermined unit, and the decoding unit outputs the conversion data for each of the predetermined units. 2. The data processing apparatus according to claim 1, wherein the data is decoded into the original data. 11. The tap coefficient is set such that a prediction error of a prediction value of the original data obtained by performing a predetermined prediction operation using the tap coefficient and the conversion data is statistically minimized. The data processing apparatus according to claim 1, wherein the data processing apparatus is obtained by performing learning. 12. The data processing apparatus according to claim 1, wherein the original data is moving image or still image data. 13. A data process for processing encoded data including at least transform data obtained by performing an orthogonal transform process or a frequency transform process and predetermined additional information, and decoding the transform data into original data. A classifying step of classifying, based on the additional information, data of interest of the original data into one of several classes; and learning. Obtaining the tap coefficient corresponding to the class of the data of interest, from among the tap coefficients for each predetermined class obtained by the above, using the conversion data and the tap coefficient corresponding to the class of the data of interest. Decoding the converted data into original data by performing a predetermined prediction operation. Data processing method. 14. A data process for processing encoded data including at least transform data obtained by performing an orthogonal transform process or a frequency transform process and predetermined additional information, and decoding the transform data into original data. Is a recording medium in which a program for causing a computer to execute is recorded, based on the additional information, of the target data of interest out of the original data, one of several classes A class classification step of classifying into the class, an acquisition step of acquiring a tap coefficient corresponding to the data of interest among tap coefficients for each predetermined class obtained by performing learning, the conversion data, and the interest By performing a predetermined prediction operation using the tap coefficient corresponding to the class of data, the converted data is converted into the original data. And a decryption step of decrypting the information. 15. A data processing device that learns tap coefficients used for decoding at least transform data obtained by performing an orthogonal transform process or a frequency transform process by a prediction operation, wherein the teacher data serves as a teacher. At least, generating means for generating student data to be a student by performing orthogonal transformation or frequency conversion, based on predetermined additional information used when generating the student data in the generating means, A classifying means for classifying the noted teacher data of interest into one of several classes; and performing a prediction operation using tap coefficients and student data corresponding to the class of the noted teacher data. The learning is performed so that the prediction error of the prediction value of the teacher data obtained by performing And a learning means for calculating the tap coefficient for each class. 16. The learning means so as to statistically minimize a prediction error of a predicted value of the teacher data obtained by performing a linear primary prediction operation using the tap coefficient and the student data. The data processing apparatus according to claim 15, wherein the data processing is performed. 17. The method according to claim 17, wherein the generating unit generates the teacher data by
16. The data processing apparatus according to claim 15, wherein the student data is generated by performing an orthogonal transformation or a frequency transformation and further performing quantization. 18. The data processing apparatus according to claim 17, wherein the additional information is a quantization table used for quantizing the teacher data. 19. The generation means, wherein the teacher data is
16. The data processing apparatus according to claim 15, wherein the student data is generated by performing orthogonal transformation or frequency transformation, quantizing, and further performing inverse quantization. 20. The generation means, comprising:
16. The data processing apparatus according to claim 15, wherein the student data is generated by at least performing a discrete cosine transform. 21. Extracting the student data used with the tap coefficients to predict the noted teacher data,
The apparatus further includes a prediction tap extracting unit that outputs a prediction tap, wherein the learning unit statistically minimizes a prediction error of a prediction value of the teacher data obtained by performing a prediction operation using the prediction tap and the tap coefficient. 16. The data processing device according to claim 15, wherein learning is performed such that 22. A class tap extracting means for extracting the student data used for classifying the teacher data of interest into one of several classes and outputting the student data as a class tap, The data processing apparatus according to claim 15, wherein the means obtains a class of the teacher data of interest based on the additional information and a class tap. 23. The generation means, comprising:
16. The data processing apparatus according to claim 15, wherein the student data is generated by performing at least orthogonal transformation processing or frequency transformation for each predetermined unit. 24. The data processing apparatus according to claim 15, wherein the teacher data is moving image or still image data. 25. A data processing method for learning a tap coefficient used for decoding at least transform data obtained by performing an orthogonal transform process or a frequency transform process by a predictive operation, comprising: , At least, a generation step of generating student data to be a student by performing orthogonal transformation or frequency conversion, based on predetermined additional information used when generating the student data in the generation step, A classifying step of classifying the noted teacher data of interest into one of several classes; and performing a prediction operation using tap coefficients and student data corresponding to the class of the noted teacher data. The prediction error of the prediction value of the teacher data obtained by performing A learning step of performing learning as described above to obtain the tap coefficient for each class. 26. A program for causing a computer to perform at least data processing for learning tap coefficients used for decoding transform data obtained by performing orthogonal transform processing or frequency transform processing by predictive calculation is recorded. A generating step of generating, at least, orthogonal transformation or frequency conversion of teacher data to be a teacher to generate student data to be a student; and A classifying step of classifying the noted teacher data of interest into any of several classes, based on the predetermined additional information to be obtained, It is obtained by performing a prediction operation using the corresponding tap coefficient and student data. A learning step of learning so that a prediction error of a prediction value of the teacher data is statistically minimized, and a learning step of obtaining the tap coefficient for each class. .