JP2005151599A - Image information processing apparatus and image information processing method - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an image information converting apparatus, capable of preventing the image quality of a converted image from being deteriorated with a simple configuration, when converting the image information of a normal resolution into image information of high resolution. <P>SOLUTION: The image information processing apparatus for converting a first image signal into a second image signal, having higher image quality comprises a peripheral pixel selecting means for selecting the predetermined number of peripheral pixels which are positioned around an attentional pixel, from the first image signal, and a predictive arithmetic means for generating the attentional pixel, by adding the products of the plurality of selected pixels and the predetermined number of coefficients, the peripheral pixel selecting means being adapted to select different peripheral pixels according to the positional relation of the target pixels and the first image signal. <P>COPYRIGHT: (C)2005,JPO&NCIPI

Description

  The present invention relates to an image information processing apparatus and an image information processing method, and is particularly suitable for being applied to an apparatus that converts normal-resolution image information into high-resolution image information and outputs it.

  2. Description of the Related Art Conventionally, a television receiver capable of obtaining a higher resolution image has been desired due to an increase in audio visual orientation, and in response to this, a so-called high vision television receiver has been developed. In this high vision system, the number of scanning lines is 525, which is more than twice the number of scanning lines defined by the so-called NTSC video signal, and the aspect ratio of the display screen is also NTSC. The wide-angle screen is 9:16 compared to 3: 4. As a result, it is possible to obtain a high-resolution, realistic screen like a theater.

  Here, although it is a high vision system having such excellent characteristics, even if an NTSC video signal is supplied as it is, an image cannot be displayed. This is because the standards differ between the NTSC system and the high vision system as described above. For this reason, when an image corresponding to the NTSC video signal is to be displayed by the high vision system, the video signal rate has been converted using a conventional image information converter.

  That is, as shown in FIG. 6, the image information conversion apparatus includes a horizontal interpolation filter 101 that performs horizontal interpolation processing of an NTSC video signal supplied via an input terminal 100, and a horizontal interpolation process. And a vertical interpolation filter 102 which performs vertical interpolation processing of the received video signal. In practice, the horizontal interpolation filter 101 is configured as a digital filter as shown in FIG. 7, and an NTSC video signal is supplied to the first to mth multipliers 111 to 111m via the input terminal 110, respectively. The Each multiplier 111 multiplies the input video signal by coefficients α0 to αm and outputs the result.

  As a result, the video signals multiplied by the coefficients α0 to αm are supplied to the first to mth adders 112 to 112m−1, respectively. Between the adders 112 to 112m-1, delay registers 113 to 113m of time T are provided, respectively. The video signal output from the mth multiplier 111m is delayed by the time T in the mth delay register 113m and supplied to the m-1th adder 112m-1. The m−1th adder 112m−1 adds the video signal delayed by the time T by the mth delay register 113m and the video signal from the m−1th multiplier 111m−1 and outputs the result. .

  The added video signals are delayed again by time T in the (m-1) th delay register 113m-1, and the (m-2) th multiplication is performed in the (m-2) th adder 112m-2 (not shown). It is added to the video signal from the device 112m-2 (not shown). In this way, the horizontal interpolation filter 101 interpolates in the horizontal direction of the NTSC video signal and supplies it to the vertical interpolation filter 102 via the output terminal 120.

  Similar to the horizontal interpolation filter 101, the vertical interpolation filter 102 is a digital filter, and performs vertical pixel interpolation on the horizontally interpolated video signal. In this way, a video signal having a rate corresponding to the high-vision video signal is obtained from the NTSC video signal, and this video signal is supplied to the high-vision receiver. As a result, an image corresponding to an NTSC video signal can be displayed on a high vision receiver.

  However, in the above-described image information conversion apparatus, the horizontal direction and the vertical direction are merely interpolated based on the NTSC video signal, so that the resolution is not different from that of the base NTSC video signal. It was. In particular, when normal video is to be converted, vertical interpolation is generally performed by in-field processing. In this case, however, the correlation between images is not used. Has a defect that the resolution is deteriorated as compared with the NTSC video signal.

  On the other hand, as an image information conversion device, the image signal level, which is an input signal, is divided into classes according to three dimensions, that is, a spatio-temporal distribution, and a memory storing a prediction coefficient value obtained by learning in advance for each class There is a method using a method of outputting an optimum estimated value by means of a calculation based on a prediction formula (Japanese Patent Application No. 5-127617).

  In this method, when creating a pixel of a high vision system (hereinafter referred to as HD (high definition) indicating high resolution), a plurality of NTSC systems (hereinafter referred to as standard resolution) existing in the vicinity thereof in terms of time and space. By dividing the pixel data of SD (standard definition) indicating the class into classes and acquiring the prediction coefficient value for each class by learning, the correlation in the time direction such as between fields and between frames in the still image portion In the image motion part, only the intra-field correlation is used to obtain an HD pixel value close to the true value.

  In practice, in FIG. 8, the SD pixel is indicated by a large “◯” and the HD pixel is indicated by a small “O”. In this figure, when the difference value between the SD pixel x1 and the SD pixel x2 is small, there is a high possibility that the image around the HD pixel y to be created is still. Therefore, in the image information conversion apparatus, the HD pixel y is created with emphasis on the SD pixel x1 and the SD pixel x2 that are close to each other in space. On the other hand, when the difference value between the SD pixel x1 and the SD pixel x2 is large, there is a high possibility that the image around the HD pixel y to be created is moving. Therefore, in the image information conversion apparatus, the HD pixel y is created by placing emphasis on the SD pixel x3 and the SD pixel x4 that are close in time.

  According to this method, since still / motion switching can be expressed smoothly by learning using an actual image, the occurrence of unnaturalness due to switching of still / motion is greatly increased as in the conventional motion adaptation method. Can be reduced. However, in this method, it is necessary to express motion information and a waveform in the space by dividing a finite number of classes, and depending on the class, patterns that should be separated normally are mixed in one class. There was a case.

  For example, in FIG. 8, when the difference value between the SD pixel x1 and the SD pixel x2 is small, it is highly possible that the surrounding image is still as described above, but it is actually a slight possibility. May have moving images. For example, when a moving object has entered only the (k + 1) field, and the object in the image is moving between the k field and the (k + 2) field, the data of the SD pixel x1 and the SD pixel x2 happen to be close to each other. For example, if

  Even in such a case, since the difference value between the SD pixel x1 and the SD pixel x2 is small, in the image information conversion apparatus, the HD pixel y is created with emphasis on the SD pixel x1 and SD pixel x2 that are close to each other in spatial position. Therefore, in this case, the error between the created HD pixel and the true HD pixel becomes large, and deterioration of the image quality is conspicuous in the converted image. As a countermeasure, it is conceivable to reduce the degradation of the image quality of the converted image by increasing the number of classes by increasing the number of pixels used for class division.

  However, if it does in this way, the number of classes will become very large, the hardware scale will increase correspondingly, and realization will become difficult. Further, the above-described method has a drawback that the hardware scale becomes large because data for three fields is used. In addition, in the current situation where motion and stillness are not completely separated, when a scene change occurs, the image quality of the converted image may be further significantly deteriorated, and the image quality of the converted image as a whole is still insufficient for practical use. It was hot.

  The present invention has been made in consideration of the above points, and image information that can prevent deterioration of the image quality of a converted image with a simple configuration when converting normal-resolution image information into high-resolution image information. A conversion device is to be proposed.

  In order to solve such a problem, in the present invention, in an image information processing apparatus that converts a first image signal into a second image signal with higher image quality, for the pixel of interest in the second image signal, the periphery of the pixel of interest Peripheral pixel selection means for selecting a predetermined number of peripheral pixels located in the first image signal from the first image signal, and prediction calculation means for generating a pixel of interest by product-sum operation of a plurality of selected pixels and a predetermined number of coefficients The peripheral pixel selecting means selects different peripheral pixels according to the positional relationship between the target pixel and the first image signal.

  According to the present invention, in the image information processing apparatus that converts the first image signal into the second image signal with higher image quality, the peripheral pixel located around the target pixel in the second image signal. A peripheral pixel selecting means for selecting a predetermined number of the first image signal from the first image signal, and detecting a level distribution pattern of the first image signal based on the peripheral pixel, and determining a class to which the target pixel belongs based on the pattern Class detection means for outputting information, and prediction calculation means for generating the target pixel by product-sum operation of a predetermined number of pixels in the first image signal around the target pixel and a predetermined number of coefficients determined according to the class detection means And the peripheral pixel selecting means selects different peripheral pixels according to the positional relationship between the target pixel and the first image signal.

  According to the present invention, for a target pixel in the second image signal, a predetermined number of different peripheral pixels are selected from the first image signal according to the positional relationship between the target pixel and the first image signal, and selected. By generating a pixel of interest by multiplying and calculating a plurality of pixels and a predetermined number of coefficients, it is possible to realize an image information processing apparatus that can prevent deterioration in image quality of a converted image with a simple configuration.

  Hereinafter, an embodiment of the present invention will be described in detail with reference to the drawings.

(1) Image Information Conversion Apparatus of Embodiment FIG. 1 shows an image information conversion apparatus as an image information processing apparatus according to the present invention as a whole. As image information supplied from the outside through an input terminal 1, an NTSC video signal is shown. Is digitized and input as SD data. FIG. 2 shows the positional relationship between SD pixels and HD pixels to be created in this image information conversion apparatus. That is, there are two types of HD pixels to be created: an HD pixel y1 present at a position close to the SD pixel and an HD pixel y2 present at a position far from the SD pixel when viewed in the same field. In this embodiment, a mode for estimating an HD pixel existing at a position close to an SD pixel is referred to as mode 1, and a mode for estimating an HD pixel existing at a position far from the SD pixel is referred to as mode 2.

  The area dividing circuit 2 divides the SD data supplied from the input terminal 1 into a plurality of areas. In this embodiment, for example, three upper and lower SD pixels in the same frame of HD pixels to be created are divided into a total of 6 pixels of 1 pixel × 6 lines. With respect to mode 1, as shown in FIG. 3, SD pixels x1, x2, x3, x4, x5, x6 corresponding to HD pixel y1 correspond to this region, and this region is referred to as block 1. As for mode 2, as shown in FIG. 4, the SD pixels x1, x2, x3, x4, x5, x6 corresponding to the HD pixel y2 correspond to that area, and this area is referred to as block 2. 2 to 4, SD pixels are indicated by large “◯”, and HD pixels are indicated by small “◯”.

  The SD data blocked by the region dividing circuit 2 in this way is supplied to an ADRC (adaptive dynamic range coding) circuit 3 and a delay circuit (DL) 6. The delay circuit 6 delays the SD data by a time necessary for processing of the ADRC circuit 3, the class code generation circuit 4, and the read only memory (ROM) table 5 and outputs the delayed SD data to the estimation arithmetic circuit 7. The ADRC circuit 3 detects a one-dimensional or two-dimensional level distribution pattern of the SD data supplied for each area, and converts the data of each area from the 8-bit SD data to, for example, 2 bits. The compressed data is calculated so as to be compressed into SD data to form pattern compressed data, and this pattern compressed data is supplied to the class code generating circuit 4.

  Here, ADRC is an adaptive requantization method originally developed for high-efficiency coding for video tape recorders (VTRs). However, since a local pattern of a signal level can be efficiently expressed with a short word length, In this embodiment, a code for classifying a signal pattern is generated using the ADRC method. The ADRC circuit 3 uses the following equation, where DR is the dynamic range in the region, n is the bit allocation, L is the data level of the pixel in the region, and Q is the requantization code.

Thus, the re-quantization is performed by equally dividing the maximum value MAX and the minimum value MIN in the region with the specified bit length. In equation (1), [] means truncation processing. In this embodiment, each 6-pixel SD data separated by the region separation circuit 2 is compressed to 2 bits. The SD data compressed in this way is defined as q1 to q6, respectively.

  The class code generation circuit 4 is based on the pattern compression data supplied from the ADRC circuit 3 and

By performing the above operation, the class to which the block belongs is detected, and a class code class indicating the class is supplied to the ROM table 5. This class code class indicates a read address from the ROM table 5.

  In practice, in the ROM table 5, by learning the relationship between the SD data pattern and the HD data, coefficient data for calculating the HD data corresponding to the SD data using the linear estimation formula is stored for each class. It is remembered. This is information for converting SD data into HD data conforming to a so-called high vision standard, which is image information with a resolution higher than that of the image information, using a linear estimation formula. In this embodiment, the coefficient data is prepared independently for mode 1 and mode 2. A method for creating coefficient data stored in the ROM table 5 will be described later. From the ROM table 5, wi (class) which is coefficient data of the class is read from the address indicated by the class code class. This coefficient data is supplied to the estimation arithmetic circuit 7.

  The estimation operation circuit 7 corresponds to the input SD data based on the SD data supplied from the area dividing circuit 2 via the delay circuit 6 and the coefficient data wi (class) supplied from the ROM table 5. HD data to be calculated is calculated. In practice, the estimation calculation circuit 7 uses the coefficient for block 1 for mode 1 and the coefficient for block 2 for mode 2 based on the SD data supplied from the delay circuit 6 and the coefficient data supplied from the ROM table 5. Based on wi (class) which is coefficient data,

By calculating the above, the HD data corresponding to the input SD data is calculated. The created HD data is supplied to the horizontal interpolation filter 8.

  The horizontal interpolation filter 8 is configured in the same manner as the horizontal interpolation filter 102 of FIG. 6 described above with respect to the prior art, and doubles the number of pixels in the horizontal direction by interpolation processing. The output of the horizontal interpolation filter 8 is output via the output terminal 9. The HD data output via the output terminal 9 is supplied to, for example, an HD television receiver or an HD video tape recorder device.

  According to the above configuration, coefficient data for estimating HD data corresponding to SD data is obtained by learning in advance for each class, stored in the ROM table 5, and input SD data and ROM table 5 Compared with the case where the input SD data is simply subjected to the interpolation processing by performing calculation based on the coefficient data read out from, and forming and outputting HD data corresponding to the input SD data, the actual data It is possible to convert to HD data closer.

  Furthermore, according to the above-described configuration, the conventional method for classifying the image information described above represents motion using the difference between the data of one frame, but is continuous in the vertical direction within one frame. By classifying only a plurality of two-field pixels by dividing the region, it is possible to express a motion using a plurality of continuous pixels. For example, the quantized data q1 to q6 as a result of 2-bit ADRC of the SD pixels x1 to x6 of the block 1 for estimating the HD pixel y1 in FIG. 3 are sequentially 2, 0, 2, 0, 2, 0, respectively. In such a case, there is a high possibility that the image is a motion image. However, since the motion is expressed using a plurality of continuous pixels in this way, the motion can be expressed with higher accuracy than before.

  Furthermore, according to the above-described configuration, since pixels other than data belonging to the same frame are not used for estimation, the hardware burden is reduced as compared with the conventional image information conversion apparatus using a total of three fields of data, Further, even when a scene change occurs, it is possible to prevent the possibility that the quality of the converted image will deteriorate.

(2) Method for Creating ROM Table Here, a method for creating coefficient data stored in the ROM table 5 will be described with reference to FIG. In order to obtain the coefficient data by learning, first, an SD image having a number of pixels of 1/4 of the HD image is formed corresponding to the already known HD image. In practice, the ideal filter circuit shown in FIG. 5 thins out the vertical pixels of the HD data supplied via the input terminal 21 so that the vertical thinning filter 22 reduces the vertical frequency in the field to ½. Further, the horizontal thinning filter 23 thins out the pixels in the horizontal direction of the HD data to obtain SD data.

  The SD data obtained as a result is supplied to the area dividing circuit 24 and divided into a plurality of areas. The area dividing circuit 24 has the same configuration as the area dividing circuit 2 of the image information conversion apparatus in FIG. 1, and divides SD data into areas each consisting of 6 pixels. That is, area division of the block 1 area is performed for the mode 1, and area division of the block 2 is performed for the mode 2. The SD data for each area is supplied to the ADRC circuit 25 and the normal equation adding circuit 27.

  The ADRC circuit 25 detects a one-dimensional or two-dimensional level distribution pattern of the SD data supplied for each area, and converts all data or a part of data in each area as described above, for example, 8 bits. The pattern compression data is formed by calculating so as to be compressed from the SD data to the 2-bit SD data, and the pattern compression data is supplied to the class code generation circuit 26. The ADRC circuit 25 has the same configuration as the ADRC circuit 3 of FIG. The SD data (SD pixels x1 to x6 in FIGS. 3 and 4) separated by the area dividing circuit 24 and having 6 pixels is compressed to 2 bits each by ADRC.

  The class code generation circuit 26 also has the same configuration as the class code generation circuit 4 of FIG. 1, and the block is obtained by performing the calculation of the expression (2) based on the pattern compression data supplied from the ADRC circuit 25. The class to which it belongs is detected, and the class code indicating that class is output. The class code generation circuit 26 outputs the class code to the normal equation addition circuit 27.

  Here, as an explanation of the normal equation adding circuit 27, learning of a conversion formula from a plurality of SD pixels to HD pixels and signal conversion using the prediction formula will be described. In the following, for the sake of explanation, a case where prediction is performed using n pixels by generalizing pixels will be described. Assume that the SD pixel levels are x1,..., Xn, respectively, and the requantized data resulting from p-bit ADRC is q1,. At this time, the class class of this area is defined by the above-described equation (2).

  As described above, when the SD pixel level is set to x1,..., Xn and the HD pixel level is set to y, an n-tap linear estimation formula using coefficients w1,. This is expressed as

Shown in Before learning, wi is an undetermined coefficient, and learning is performed on a plurality of signal data for each class. When the number of data is m, following equation (4),

Is set. If m> n, w1,... Wn are not uniquely determined.

Defined by

Find the coefficient that minimizes. This is a so-called least square method.

  Here, the partial differential coefficient by wi in the equation (7) is obtained. It is the following formula

Each wi should be obtained so that 0 becomes zero. The following formula

And the following formula

If Xji and Yi are defined as follows, the above equation (8) uses the matrix and

To be rewritten.

  This equation is generally called a normal equation. The normal equation adding circuit 27 includes HD pixels corresponding to the class code supplied from the class code generating circuit 26, the SD data x1,..., Xn supplied from the area dividing circuit 24, and the SD data supplied from the input terminal 21. The normal equation is added using the level y.

  After all the training data has been input, the normal equation adding circuit 27 outputs the normal equation data to the prediction coefficient determining circuit 28. The prediction coefficient determination circuit 28 calculates a prediction coefficient by solving for w i by using a general matrix solving method such as sweeping out a normal equation. The prediction coefficient determination circuit 28 writes the calculated prediction coefficient in the memory 29.

  As a result of performing the training as described above, the memory 29 is estimated to be close to a true value in terms of statistics for estimating the target HD data y for each pattern defined by the quantized data q1,. The prediction coefficient that can be stored is stored. The table stored in the memory 29 is the ROM table 5 used in the image information conversion apparatus described above with reference to FIG. With the above processing, the learning of coefficient data for creating HD data from SD data using the linear estimation formula is completed.

(3) Other Embodiments In the above-described embodiments, the case where image data information is compressed and classified using the compression encoding method by ADRC has been described. However, the compression method is not limited to this. Any method may be used as long as it is a compression coding that can represent information of image data in a class having a few signal waveform patterns. For example, differential quantization (DPCM), vector quantization (VQ) may be used. And various methods such as discrete cosine transform (DCT) are conceivable.

  In the above-described embodiment, for the sake of simplification of explanation, the horizontal interpolation filter is used to convert the image information in the horizontal direction. Instead, a ROM table for converting the image information in the horizontal direction is used. It is also possible to prepare and convert the image information using the estimation formula also in the horizontal direction.

  In the above embodiment, the signal waveform pattern is divided and expressed one-dimensionally by the area dividing circuit. Instead, the signal waveform pattern is divided and expressed two-dimensionally. However, the same effect as the above-described embodiment can be realized.

  In the above-described embodiment, the SD pixels used for class classification and the SD pixels used in the linear estimation formula are the same, but they are not necessarily the same. Incidentally, when different pixels are used, it is desirable that the SD pixels used for classification are included in the SD pixels used in the linear estimation formula, and the SD pixels used in the linear estimation formula used additionally. Are preferably only those belonging to the same field as the HD pixel to be estimated.

  In the above-described embodiment, the case where the coefficient data stored in the ROM table is used when converting the image information has been described. Instead of this, the ROM table is represented by the center of gravity method according to the class classification. By storing the value, the HD data may be interpolated without performing the estimation calculation.

  In the above-described embodiments, the present invention has been described with respect to the case where the NTSC video signal is converted into the high vision video signal. However, the present invention is not limited to this, and the resolution of the first image information can be set to The present invention can be widely applied to the case of converting to second image information having a higher resolution than the resolution of one image information.

  In the above-described embodiments, the present invention has been described as functioning alone as an image information conversion apparatus. However, the image information conversion apparatus according to the present invention is not limited to this, for example, a television receiver, a video tape recorder apparatus, It may be built in a computer device or the like or added as a peripheral device.

  The image information processing apparatus and the image information processing method of the present invention can be applied to an apparatus that converts normal resolution image information into high resolution image information and outputs the converted information.

It is a block diagram which shows the structure of one Example of the image information converter by this invention. It is a basic diagram which shows the positional relationship of SD data and HD data as a premise of the image information conversion by this invention. It is a basic diagram which shows the positional relationship of SD data and HD data as description of the data used for class division. It is a basic diagram which shows the positional relationship of SD data and HD data as description of the data used for class division. FIG. 5 is a block diagram for explaining a method for creating a ROM table. It is a block diagram of the conventional image information converter. FIG. 7 is a connection diagram illustrating a configuration of a horizontal interpolation filter of the image information conversion apparatus in FIG. 6. It is a basic diagram which shows the positional relationship of SD data and HD data as description of the image information conversion by the conventional space-time class division.

Explanation of symbols

  1, 21, 100, 110: Input terminal, 2, 24: Area division circuit, 3, 25: ADRC circuit, 4, 26: Class code generation circuit, 5: ROM table, 6: Delay Circuit, 7... Estimation operation circuit, 8, 101... Horizontal interpolation filter, 9, 103, 120... Output terminal, 22. 28... Prediction coefficient determination circuit, 29... Memory, 102... Vertical interpolation filter, 111... Multiplier, 112.

Claims (12)

In an image information processing apparatus that converts a first image signal into a second image signal having higher image quality,
With respect to the target pixel in the second image signal, peripheral pixel selection means for selecting a predetermined number of peripheral pixels located around the target pixel from the first image signal;
Prediction calculation means for generating the pixel of interest by multiplying and multiplying the selected plurality of pixels and a predetermined number of coefficients,
The image processing apparatus according to claim 1, wherein the peripheral pixel selection unit selects different peripheral pixels according to a positional relationship between the pixel of interest and the first image signal. Second peripheral pixel selection means for selecting a predetermined number of second peripheral pixels located around the target pixel from the first image signal;
Class detection means for detecting a level distribution pattern of the first image signal based on the second peripheral pixels, determining a class to which the pixel of interest belongs based on the pattern, and outputting class detection information;
Coefficient data of an estimation formula, which is information for converting the first image signal into the second image signal, is stored for each class, and the coefficient data according to the class detection information from the class detection means. And coefficient data storage means for outputting
The image information processing apparatus according to claim 1, wherein the predetermined number of coefficients are output from the coefficient data storage unit according to the class of the pixel of interest. The image information processing apparatus according to claim 2, wherein the second peripheral pixel selection unit selects different peripheral pixels according to a positional relationship between the target pixel and the first image signal. The image information processing apparatus according to claim 1, wherein the positional relationship is determined according to a distance between the target pixel and the peripheral pixels. The image information processing apparatus according to claim 1, wherein the high image quality is at least high resolution. The image information processing apparatus according to claim 1, wherein the peripheral pixels are selected from pixels in a continuous field of the first image signal. In an image information processing apparatus that converts a first image signal into a second image signal having higher image quality,
Peripheral pixel selection means for selecting a predetermined number of peripheral pixels located around the target pixel from the first image signal for the target pixel in the second image signal;
Class detection means for detecting a level distribution pattern of the first image signal based on the peripheral pixels, determining a class to which the pixel of interest belongs based on the pattern, and outputting class detection information;
Prediction calculating means for generating the pixel of interest by a product-sum operation of a predetermined number of pixels of the first image signal around the pixel of interest and a predetermined number of coefficients determined according to the class detecting means,
The image processing apparatus according to claim 1, wherein the peripheral pixel selection unit selects different peripheral pixels according to a positional relationship between the pixel of interest and the first image signal. Coefficient data of an estimation formula, which is information for converting the first image signal into the second image signal, is stored for each class, and the coefficient data according to the class detection information from the class detection means. Is further provided with coefficient data storage means for outputting
The image information processing apparatus according to claim 7, wherein the predetermined number of coefficients are output from the coefficient data storage unit according to the class of the target pixel. The image information processing apparatus according to claim 7, wherein the positional relationship is determined according to a distance between the target pixel and the peripheral pixel. The image information processing apparatus according to claim 7, wherein the peripheral pixels are selected from pixels in a continuous field of the first image signal. In an image information processing method for converting a first image signal into a higher-quality second image signal,
A first step of selecting a predetermined number of peripheral pixels located around the target pixel from the first image signal for the target pixel in the second image signal;
A second step of generating the pixel of interest by a product-sum operation of the selected plurality of pixels and a predetermined number of coefficients,
In the first step, different peripheral pixels are selected according to a positional relationship between the pixel of interest and the first image signal. In an image information processing method for converting a first image signal into a higher-quality second image signal,
A first step of selecting a predetermined number of peripheral pixels located around the target pixel from the first image signal for the target pixel in the second image signal;
A second step of detecting a level distribution pattern of the first image signal based on the peripheral pixels, determining a class to which the pixel of interest belongs based on the pattern, and outputting class detection information;
A third step of generating the pixel of interest by a product-sum operation of a predetermined number of pixels of the first image signal around the pixel of interest and a predetermined number of coefficients determined according to the class detection means,
In the first step, different peripheral pixels are selected according to a positional relationship between the pixel of interest and the first image signal.

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