JP3480461B2 - Digital image signal processing apparatus and processing method - Google Patents

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a digital image signal processing apparatus and processing method applied to receive a sub-sampling signal and interpolate a thinned pixel.

[0002]

2. Description of the Related Art As one method for band compression or information amount reduction when recording or transmitting a digital image signal, there is a method of reducing the amount of transmission data by thinning out pixels by subsampling. One example is the multiple sub-Nyquist sampling encoding method in the MUSE method. In this system, it is necessary to interpolate non-transmitted pixels that have been decimated on the receiving side.

Offset subsampling is known as an example of subsampling. FIG. 7 shows an example of the offset sub-sampling circuit, in which a digital video signal is supplied to the input terminal 61 and is supplied to the sub-sampling circuit 63 via the pre-filter 62.
A sampling pulse having a predetermined frequency is supplied to the sub-sampling circuit 63 from the input terminal 64.

FIG. 8 shows an example of two-dimensional offset subsampling performed by the subsampling circuit 63.
The sampling interval (Tx, Ty) in the horizontal direction (x direction) and the vertical direction (y direction) is the pixel interval (H
(x, Hy) twice, thinning out every other pixel (thinning pixels are indicated by x), and transmitting pixels (indicated by ○) adjacent in the vertical direction are half the sampling interval (Tx / 2).
It only offsets. The transmission band obtained by performing such offset sub-sampling can widen the horizontal or vertical spatial frequency component with respect to the diagonal spatial frequency.

The output signal of the sub-sampling circuit 63 is taken out to the output terminal 66 via the post filter 65. The pre-filter 62 limits the band of the image signal to be sampled, and the post-filter removes unnecessary or harmful signal components. The amount of data transmitted by the sub-sampling can be reduced, and the digital video signal can be transmitted through a relatively low speed transmission line. When the received offset sub-sampled image signal is displayed on the monitor or printed out, the thinned pixels are interpolated using the adjacent pixels.

By the way, the offset sub-sampling as described above is a very effective method when the pre-filter before the sampling is correctly performing the filtering process. However, if the pre-filter cannot be applied sufficiently or the pre-filter is not applied sufficiently to widen the transmission band, the problem of image quality deterioration due to aliasing distortion occurs.

In order to reduce the occurrence of the above-mentioned folding distortion,
Adaptive interpolation methods have been proposed. This is a method in which the optimum interpolation method is determined in advance during subsampling, and the determination result is transmitted or recorded as auxiliary information. For example, which one of the horizontal half average value interpolation and the vertical half average value interpolation is closer to the true value is detected at the time of sub-sampling and transmitted as 1 bit of auxiliary information per pixel. However, at the time of interpolation, interpolation processing is performed according to this auxiliary information.

In the above-mentioned adaptive interpolation method using the auxiliary information, it is necessary to transmit the auxiliary information in addition to the transmission pixels, which causes a problem that the compression rate of the data amount is lowered. Further, when an error occurs in the auxiliary information in the process of transmission or recording / reproduction, there is a drawback that the reproduced image is likely to be deteriorated because incorrect interpolation is performed.

As one method for solving this problem, Japanese Patent Laid-Open No. 63-48088 proposed by the applicant of the present application describes the value of a pixel of interest by a linear linear combination of pixels around it and a coefficient, and It has been proposed to determine the value of this coefficient by the method of least squares using the actual value of the pixel of interest so that the sum of squares of is minimized. Here, the coefficient of the linear linear combination is previously determined by learning, and the coefficient of determination is stored in the memory. Furthermore, when interpolating the pixel of interest,
The average value of the surrounding reference pixels is calculated, and each pixel is represented by 1 bit according to the magnitude relationship between the average value and the value of each pixel,
The classification is performed according to the pattern of (the number of reference pixels × 1 bit), and the interpolated value reflecting the local feature of the image including the target pixel is formed. This method can interpolate thinned pixels well without the need for auxiliary information.

[0010]

On the other hand, considering the transmission pixels, the pre-filter 32 and the post-filter 3 are considered.
Since the signal is transmitted through the signal 5, the high frequency component is lost, resulting in the problem that the signal waveform is rounded. That is, the filtering process required for subsampling also adversely affects the transmission pixels.

Therefore, it is an object of the present invention to provide a digital image signal processing apparatus and a processing method capable of compensating for a band lost by a filter or the like for a transmission pixel when decoding a sub-sampling signal. .

[0012]

According to a first aspect of the present invention, a digital image signal that has passed through a pre-filter is sampled, a signal in which the number of pixels is reduced by sampling is received, and pixels thinned by sampling are interpolated. In the digital image signal processing device, the value of the transmission pixel of interest present in the received digital image signal and a plurality of transmission pixels spatially and / or temporally close to the transmission pixel of interest are used. A class classification means for determining the class of the transmission pixel of interest, and a linear linear combination of values and coefficients of a plurality of transmission pixels included in the input digital image signal and spatially and / or temporally adjacent to the transmission pixel of interest. , When the value of the transmission pixel of interest is created, the coefficient for each class is set so as to minimize the error between the created value and the true value of the transmission pixel of interest. Previously obtained by learning, class content
Corrected by a linear linear combination of the coefficient generating means for generating a coefficient corresponding to the class from the similar means and the values of a plurality of transmission pixels spatially and / or temporally adjacent to the transmission pixel of interest. A first digital image signal at the time of learning, having an arithmetic means for generating the value of the transmission pixel of interest.
And supply the first digital image signal to the pre-filter
Subsampling the output of the prefilter.
A digital image signal processing device, characterized in that a coefficient is obtained using the second digital image signal .

According to a second aspect of the present invention, the digital image signal is sampled through the pre-filter, the signal whose number of pixels is reduced by the sampling is received, and the pixels thinned by the sampling are interpolated. In the signal processing device, a value of the transmission pixel of interest present in the received digital image signal and a plurality of transmission pixels spatially and / or temporally close to the transmission pixel of interest are used to determine the class of the transmission pixel of interest. and class classification means for determining, in advance representative values acquired by learning are stored for each class, and a memory means for outputting a representative value corresponding to the class determined by the classification means as the value of the target transmission pixel , The first digital when learning
Pre-filter the image signal and the first digital image signal
And subsample the output of the prefilter.
Representative value using the second digital image signal that has been subjected to
Is a digital image signal processing device.

For a transmission pixel, a correction value, that is, a predicted transmission pixel value can be formed by a linear linear combination of a coefficient acquired by learning in advance and the values of surrounding transmission pixels. This correction value can compensate for the resolution lost in the filtering process. It is also possible to obtain an average value of the transmission pixel values or a normalized value in advance by learning and use this average value as the correction value.

[0015]

BEST MODE FOR CARRYING OUT THE INVENTION An embodiment in which the present invention is applied to a sub-sampling signal interpolating device will be described below. In FIG. 1, reference numeral 1 denotes an input terminal for an offset sub-sampled digital video signal. Specifically, a transmission signal by broadcasting or the like, a reproduction signal from a VTR or the like is supplied to the input terminal 1. The value of the transmission pixel is represented by an 8-bit code. Reference numeral 2 is a time series conversion circuit for converting an input signal that arrives in raster order in the order of blocks.

The output signal of the time series conversion circuit 2 is supplied to the class classification circuits 3 and 4. The class classification circuit 3 determines the class of the target thinned pixel to be interpolated,
A class code indicating the class is supplied to the memory 5 as an address. The class classification circuit 4 determines the class of the target transmission pixel to be corrected, and a class code indicating the class is supplied to the memory 6 as an address. The prediction coefficient read from the memory 5 is supplied to the interpolation value generation circuit 7, and the prediction coefficient read from the memory 6 is supplied to the correction value generation circuit 8.

As will be described later, the memories 5 and 6 store prediction coefficients previously acquired by learning. This coefficient is required to predict the interpolation value of the thinned pixel and the correction value of the transmission pixel, respectively. Both the interpolation value and the correction value are prediction values, but the prediction value for the thinned pixel is called an interpolation value, and the prediction value for the transmission pixel is called a correction value. Interpolation value generation circuit 7 and correction value generation circuit 8
For, the values of a plurality of pixels around the target pixel are supplied from the time series conversion circuit 2. Then, the interpolation value generation circuit 7
Generates a predicted value of the thinned pixel of interest by a linear linear combination of the coefficient from the memory 5 and the values of surrounding transmission pixels.
Similarly, the correction value generation circuit 8 generates the correction value of the target transmission pixel by linear linear combination of the coefficient from the memory 6 and the values of the surrounding transmission pixels.

The generated correction value and the generated interpolation value are supplied to the synthesizing circuit 9, the thinned pixels are interpolated at the output terminal 10, and the frequency component lost by the filtering process is compensated for. Is output. Although not shown, a time series conversion circuit is connected to the output terminal 10 to form a digital video signal converted from the block order to the raster scan order.

The class classification circuit 3 uses the values of the transmission pixels in the vicinity of the target thinned pixel to determine the class of the target thinned pixel. As shown in FIG. 2A, the pattern of the level distribution of the transmission pixels (A, B, C, D) on the upper, lower, left and right sides of the target thinning pixel is determined as a class. As an example, the average value Av of the four referenced pixels is obtained, and the surrounding pixels are compressed from 8 bits to 1 bit according to the magnitude relationship with the average value Av. That is, as shown in an example in FIG.
If the value is larger than the average value Av, `1` is assigned, and if the value is smaller than the average value Av,` 0` is assigned. In the example of FIG. 3, the class code (1010) is generated from the class classification circuit 3.

Similarly, the class classification circuit 4 determines the class of the transmission pixel of interest. As shown in FIG. 2B, classification is performed using the target transmission pixel (whose value is y) and the upper, lower, left, and right transmission pixels a, bc, and d. Unlike the thinned-out pixel, the value y of the pixel of interest exists, so this value y is used in the case of classification. For example, the value y (8 bits) of the pixel of interest is compressed to 3 bits, the values a and c of the upper and lower pixels are compressed to 1 bit, and the values b and d of the left and right pixels are compressed to 2 bits. Then, the total 9 bits are used as the class code.

The reason for compressing the number of bits in this way is that if 8-bit pixel data is used as it is, the number of classes becomes enormous, and the memory capacity and the scale of hardware such as a memory control circuit become too large. is there. As a concrete method of compressing the bit number of the transmission pixel to be referred to in order to obtain an appropriate number of classes, various methods are possible without being limited to the method of using the average value.

One of them is ADRC (Adaptive Dynamic
It is compression by Range Coding). ADRC is to adaptively remove the redundancy in the level direction by utilizing the correlation within a block of an image. That is, the maximum value and the minimum value of the data in the block are detected, and the difference in the value of each pixel from the maximum value or the minimum value is reconstructed with the quantization step width according to the dynamic range (difference between the maximum value and the minimum value). Quantize. The value of each pixel can be compressed to a predetermined number of bits by ADRC. For example, when compressing to 1 bit, the values a to d of the reference transmission pixels are divided by the dynamic range, and the quotient is compared with 0.5. It is encoded as `0 '. 1-bit AD
When using RC, a total of 7 bits of 4 bits corresponding to the values a to d of four surrounding pixels and 3 bits of the own value compressed by another requantization are the class code. To be done.

Not only ADRC but DPCM (Differentia
l Pulse Code Modulation), BTC (Block Trancation
Coding), VQ (Vector Quantization) and other compression encoding can be used.

The interpolation value generation circuit 7 generates an interpolation value by linearly combining the prediction coefficient from the memory 5 and the values of the peripheral pixels. As shown in FIG. 2A, four pixels are used for classification, but more pixel values (uncompressed) are used for prediction. Similarly, the correction value generation circuit 8 generates the correction value by linearly combining the values of the transmission pixels around the prediction coefficient from the memory 6. We do not use our own value y for this prediction. In addition, as the number of pixels for prediction, surrounding pixels having more than 4 pixels are used.

As the correction value, a correction value of the transmission pixel of interest is generated by linear linear combination using the values of a plurality of peripheral pixels and the prediction coefficient. The interpolation value generation circuit 7 also generates the interpolation value of the thinned-out pixel of interest by the linear linear combination of the prediction coefficient read from the memory 5 and the values of the surrounding transmission pixels in the same manner as described above. The prediction coefficients stored in the memories 5 and 6 are obtained by learning in advance. Learning about a coefficient for generating a correction value will be described below, but this learning method can also be applied to learning about a coefficient for generating an interpolation value.

FIG. 4 shows the structure at the time of learning for determining the prediction coefficient. The learning is performed by the same process as the process of forming the digital video signal supplied to the input terminal 1 of FIG. 1 from the original digital video signal, and the coefficient for minimizing the sum of squares of the error with respect to the true value of the transmission pixel of interest. Is determined by the method of least squares.

In FIG. 4, an original digital video signal is supplied to an input terminal indicated by 11. A prefilter 12, a subsampling circuit 13, and a postfilter 15 are connected to the input terminal 11. The sub-sampling circuit 13 is supplied from the input terminal 14 with a sampling pulse having a predetermined frequency for performing offset sub-sampling. Therefore, at the output of the post filter 15, the offset sub-sampled digital video signal is obtained.

A time series conversion circuit 16 is connected to the post filter 15, and the video data converted from the raster scan order to the block order is supplied to the class classification circuits 17 and 18. Similar to the class classification circuit 3 described above, the class classification circuit 17 determines the class of the target thinning pixel using the transmission pixels around the target thinning pixel.
Similar to the class classification circuit 4 described above, the class classification circuit 18 uses the value of the transmission pixel of interest itself and the transmission pixels in the vicinity thereof to determine the class of the transmission pixel of interest. The class codes from the class classification circuits 17 and 18 are the coefficient determination circuit 19
And 20 respectively.

The coefficient determination circuits 19 and 20 determine a prediction coefficient that minimizes the sum of squares of the error between the predicted value y'generated by linear linear combination and its true value y. The original data supplied to the input terminal 11 is supplied to the time series conversion circuit 23, and the true value of the target thinning pixel and the true value of the target transmission pixel are supplied from this circuit 23 to the coefficient determination circuits 19 and 20. Further, the transmission pixels used for prediction are supplied from the time series conversion circuit 16 to the coefficient determination circuits 19 and 20.

Each coefficient determining circuit determines the best prediction coefficient by the method of least squares. The determined prediction coefficients are stored in the memories 21 and 22, respectively. The storage address is designated by the class code from the class classification circuits 19 and 20. As an example, an operation of performing the coefficient determination process regarding the correction value of the transmission pixel by the software process will be described with reference to FIG. Control of the process is started from step 41, and in the learning data formation of step 42,
Learning data corresponding to a known image is formed. At the end of the data in step 43, all the input data such as 1
If the processing of the frame data is completed, step 4
If the prediction coefficient determination of step 6 is not completed, step 4
Control is transferred to the 4th class decision.

As described above, the class determination in step 44 is a step in which the class is determined in correspondence with the level distribution pattern of the value of the transmission pixel of interest and the values of the peripheral pixels. In the normal equation generation in the next step 45, a normal equation described later is created.

After the processing of all the data is completed from the end of the data in step 43, the control proceeds to step 46, and step 4
In the determination of the prediction coefficient of 6, the coefficient is determined by solving the equation (8) described later using the matrix solution method. The predictive coefficient is stored in the memory 22 by the predictive coefficient store in step 47, and the learning process control ends in step 48.

Step 45 in FIG. 5 (normal equation generation)
The process of step 46 (determination of prediction coefficient) will be described in more detail. At the time of learning, the true value y of the transmission pixel of interest is known. Assuming that the correction value of the target transmission pixel is y'and the values of the surrounding pixels are x1 to xn, the coefficients w1 to w for each class.
An n-tap linear linear combination by n y '= w1 x1 + w2 x2 + ... + wn xn (1) is set. Before learning, wi is an undetermined coefficient.

As mentioned above, learning is done for each class,
When the number of data is m, according to the equation (1), yj '= w1 xj1 + w2 xj2 + ... + wn xjn (2) (where j = 1,2, ... m)

When m> n, w1 to wn are not uniquely determined, so the elements of the error vector E are ej = yj- (w1 xj1 + w2 xj2 + ... + wn xjn) (3) (where j = 1, 2, , ..., M) and find the coefficient that minimizes the following equation (4).

[0036]

[Equation 1]

This is a so-called least squares method. Here, the partial differential coefficient by w i of equation (4) is obtained.

[0038]

[Equation 2]

Since each wi may be determined so that the equation (5) becomes 0,

[0040]

[Equation 3]

Using a matrix as

[0042]

[Equation 4]

It becomes This equation is generally called a normal equation. If this equation is solved for wi using a general matrix solution method such as the sweeping method, the prediction coefficient wi is obtained, and this prediction coefficient wi is stored in the memory with the class code as an address.

Although FIG. 5 shows a software structure for learning, learning can be performed by a hardware structure or a combination of software and hardware. Further, the interpolation value and the correction value are not limited to the linear linear combination using the prediction coefficient, but the data value itself may be created in advance by learning, and this value may be used as the interpolation value and the correction value.

FIG. 6 is a flow chart for explaining learning for creating the data value itself in advance.
The control start step 51, the learning data formation step 52, the data end step 53, and the class determination step 54 are the same as the steps 41, 42, 43, and 44 in the learning for determining the prediction coefficient described above. Is the step of performing.

The step 55 of determining a representative value is a step of obtaining an average value of true values for each class and determining the average value as a representative value. That is, the representative value is obtained by dividing the cumulative value of the true value obtained in the learning process by the cumulative frequency. In this case, when the data values themselves are accumulated, the accumulated data amount increases, so a value normalized by the reference value in the block and the dynamic range DR of the block may be obtained as the representative value.

That is, assuming that the reference value of the block is B (for example, the minimum value of the pixels in the block) and the dynamic range is represented by DR, the normalized representative value G is G = (y−B) / DR. Stipulated. In step 56, the determined representative value is stored in the memory, and the learning ends.

For class classification or prediction calculation according to the present invention, the values of the pixels surrounding the pixel of interest are not limited to those spatially used, but a pixel close to the pixel of interest in the time direction (for example, the same pixel in the previous frame). Pixels) can also be used. Further, the method of class classification in the present invention is not limited to the method based on the pattern of level distribution,
It may be based on the correlation direction of the image of the block including the pixel of interest.

[0049]

According to the present invention, not only the pixels thinned out by sampling but also the values of transmission pixels are corrected, so that the high frequency component lost by the filtering process for sampling can be compensated. . Therefore, the distortion of the waveform of the decoded signal can be compensated, and the quality of the decoded image can be improved.

[Brief description of drawings]

FIG. 1 is a block diagram of an embodiment of the present invention.

FIG. 2 is a schematic diagram showing positions of pixels to be referred to for classification.

FIG. 3 is a schematic diagram for explaining an example of a classification method.

FIG. 4 is a block diagram of an example of a configuration for obtaining a prediction coefficient.

FIG. 5 is a flowchart when learning for obtaining a prediction coefficient is performed by software processing.

FIG. 6 is a flowchart when learning for obtaining a representative value is performed by software processing.

FIG. 7 is a block diagram of an example of a configuration for offset subsampling.

FIG. 8 is a schematic diagram showing a structure of two-dimensional offset subsampling.

[Explanation of symbols]

3, 4 ... Class classification circuit, 4, 6 ... Memory storing prediction coefficients, 7 ... Interpolation value generation circuit, 8 ...
Correction value generation circuit

─────────────────────────────────────────────────── ─── Continuation of the front page (58) Fields surveyed (Int.Cl. ⁷ , DB name) H04N ^7/ 00-7/68

Claims (8)

(57) [Claims] 1. A digital image signal processing apparatus for sampling a digital image signal that has passed through a prefilter, receiving a signal in which the number of pixels has been reduced by the sampling, and interpolating pixels thinned out by the sampling. , The value of the transmission pixel of interest present in the received digital image signal and a plurality of transmission pixels spatially and / or temporally close to the transmission pixel of interest are used to determine the class of the transmission pixel of interest. And a linear first-order combination of values and coefficients of a plurality of transmission pixels spatially and / or temporally near the transmission pixel of interest included in the input digital image signal. When creating the value of, the coefficient that minimizes the error between the created value and the true value of the target transmission pixel Previously obtained through learning for each, or the classification means
Corrected by a coefficient generating means for generating a coefficient corresponding to these classes, and a linear linear combination of the coefficient and the values of a plurality of transmission pixels spatially and / or temporally adjacent to the transmission pixel of interest. and a computing means to generate the value of the target transmission pixel, during the training, and the first digital image signal, the first
The digital image signal of
The second subsampled output of the filter
A digital image signal processing device, characterized in that the above coefficient is obtained using a digital image signal. 2. A digital image signal processing apparatus for sampling a digital image signal that has passed through a pre-filter, receiving a signal in which the number of pixels has been reduced by the sampling, and interpolating pixels thinned by the sampling. , The value of the transmission pixel of interest present in the received digital image signal and a plurality of transmission pixels spatially and / or temporally close to the transmission pixel of interest are used to determine the class of the transmission pixel of interest. Classifying means for storing the representative value acquired by learning in advance for each class, and a memory means for outputting the representative value corresponding to the class determined by the class classifying means as the value of the noted transmission pixel. And has the first digital image signal and the first digital image signal during the learning.
The digital image signal of
The second subsampled output of the filter
A digital image signal processing apparatus, characterized in that the representative value is obtained using a digital image signal. 3. The digital image signal processing apparatus according to claim 1 or 2, wherein the interpolation circuit for interpolating the thinned pixels is spatially and / or temporally close to the thinned pixel of interest. Class classification means for determining a class of the thinning-out pixel of interest using a plurality of transmission pixels, and a plurality of transmissions included in the input digital image signal and spatially and / or temporally close to the thinning-out pixel of interest When the value of the target thinned pixel is created by linear linear combination of the pixel value and the coefficient, a coefficient for minimizing the error between the created value and the true value of the target thinned pixel is set for each class. To be obtained by learning in advance and classify into the above
The coefficient generating means for generating a coefficient corresponding to the class from the means, and the linear primary combination of the coefficient and the values of a plurality of transmission pixels spatially and / or temporally neighboring the thinning-out pixel of interest, and means for generating an interpolated value of the sampling pixel, during the training, and the first digital image signal, the first
The digital image signal of
The second subsampled output of the filter
A digital image signal processing device, characterized in that the above coefficient is obtained using a digital image signal. 4. The digital image signal processing apparatus according to claim 1 or 2, wherein the interpolation circuit for interpolating the thinned pixels is spatially and / or temporally close to the thinned pixel of interest. Class classification means for determining the class of the thinned pixel of interest using a plurality of transmission pixels, and representative values obtained by learning in advance are stored for each class and correspond to the class determined by the class classification means. to the representative value and a memory means for outputting a value of the pixel of interest, at the time of the learning, and the first digital image signal, the first
The digital image signal of
The second subsampled output of the filter
A digital image signal processing apparatus, characterized in that the representative value is obtained using a digital image signal. 5. A digital image signal processing method for sampling a digital image signal that has passed through a prefilter, receiving a signal in which the number of pixels has been reduced by the sampling, and interpolating pixels thinned by the sampling. , The value of the transmission pixel of interest present in the received digital image signal and a plurality of transmission pixels spatially and / or temporally close to the transmission pixel of interest are used to determine the class of the transmission pixel of interest. And a linear linear combination of a coefficient and a value of a plurality of transmission pixels spatially and / or temporally adjacent to the transmission pixel of interest included in the input digital image signal. When creating the value of, the coefficient is set so as to minimize the error between the created value and the true value of the transmission pixel of interest. Previously obtained through learning for each class, or the class classifying unit
Corrected by a coefficient generating step for generating a coefficient corresponding to these classes, and a linear linear combination of the coefficient and the values of a plurality of transmission pixels spatially and / or temporally neighboring the transmission pixel of interest. It consists of a calculation step for generating the value of the target transmission pixel, during the training, and the first digital image signal, the first
The digital image signal of
The second subsampled output of the filter
A digital image signal processing method, characterized in that the above coefficient is obtained using a digital image signal. 6. A digital image signal processing method for sampling a digital image signal that has passed through a prefilter, receiving a signal in which the number of pixels has been reduced by the sampling, and interpolating pixels thinned out by the sampling. , The value of the transmission pixel of interest present in the received digital image signal and a plurality of transmission pixels spatially and / or temporally close to the transmission pixel of interest are used to determine the class of the transmission pixel of interest. And a step of storing a representative value acquired by learning in advance for each class and outputting the representative value corresponding to the class determined by the class sorting step as the value of the noted transmission pixel. Tona is, at the time of the learning, and the first digital image signal, the first
The digital image signal of
The second subsampled output of the filter
A digital image signal processing method, characterized in that the representative value is obtained using a digital image signal. 7. The digital image signal processing method according to claim 5 or 6, wherein the interpolation method for interpolating the thinned pixels is a spatial and / or temporal neighborhood of the thinned pixel of interest. A classifying step of determining a class of the thinning-out pixel of interest using a plurality of transmission pixels, and a plurality of transmissions included in the input digital image signal spatially and / or temporally close to the thinning-out pixel of interest. When the value of the target thinned pixel is created by linear linear combination of the pixel value and the coefficient, a coefficient for minimizing the error between the created value and the true value of the target thinned pixel is set for each class. And a linear linear combination of the coefficient and the values of a plurality of transmission pixels spatially and / or temporally adjacent to the thinning-out pixel of interest, Ri Do and a step for generating an interpolated value of the feeder pixel, during the training, and the first digital image signal, the first
The digital image signal of
The second subsampled output of the filter
A digital image signal processing method, characterized in that the above coefficient is obtained using a digital image signal. 8. The digital image signal processing method according to claim 5 or 6, wherein the interpolation method for interpolating the thinned pixels is a spatially and / or temporally neighboring pixel of the thinned pixel of interest. A class classification step of determining the class of the thinned pixel of interest using a plurality of transmission pixels, and a representative value obtained by learning in advance is stored for each class, and corresponds to the class determined by the class classification step. the representative value of Ri Do and a step of outputting a value of the pixel of interest, at the time of the learning, and the first digital image signal, the first
The digital image signal of
The second subsampled output of the filter
A digital image signal processing method, characterized in that the representative value is obtained using a digital image signal.