JPH1127564A - Image converter, method therefor and presentation medium - Google Patents

Abstract

PROBLEM TO BE SOLVED: To surely correct an image, even if it is deteriorated. SOLUTION: A class tap is segmented by an area segmentation part 1 and classified by an ADRC pattern extraction part 4. A class code corresponding to a class and a feature amount is generated by a class code generation part 5 and supplied to a ROM table 6. A predicted coefficient corresponding to the inputted class code is outputted to a predication operation part 7 by the ROM table 6. A prediction operation is performed from a predictive tap which is segmented by an area segmentation part 2 and the predicated coefficient. Segmentation of the area segmentation part 1 and the area segmentation part 2 is controlled, dynamically, corresponding to a feature detected by a feature amount extraction part 3.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an image conversion apparatus and method, and a providing medium, and more particularly, to converting an input image signal into an image signal of the same format or a different format when converting the input image signal into an image signal of a different format. The present invention relates to an image conversion apparatus and method capable of surely providing an image signal whose image quality is corrected or whose image quality is improved even if the image quality is poor, and a providing medium.

[0002]

2. Description of the Related Art The present applicant has disclosed, for example, Japanese Patent Application Laid-Open No. 8-515.
No. 99 proposes a technique for obtaining higher resolution pixel data. In this proposal, for example, when image data composed of HD (High Definition) pixel data is created from image data composed of SD (Standard Definition) pixel data, H
Classification is performed using the SD pixel data located in the vicinity of the D pixel data (class is determined), the prediction coefficient value is learned for each class, and the intra-frame correlation is calculated in the image stationary unit. In the moving part,
Utilizing intra-field correlation, HD pixel data closer to the true value is obtained.

[0003]

By the way, by using this technique, for example, an image having very poor image quality (blurred image) can be corrected to an image having good image quality. However, in the case of image data of very poor image quality, if the classification is performed using the image data of very poor image quality,
An appropriate class classification cannot be performed, and an appropriate class cannot be determined. If an appropriate class cannot be obtained, an appropriate set of prediction coefficient values cannot be obtained, and as a result, there has been a problem that a sufficient image quality cannot be corrected.

[0004] The present invention has been made in view of such circumstances, and even if the image quality of input image data is poor,
It is an object of the present invention to provide an image conversion apparatus and method capable of surely correcting the image quality.

[0005]

According to a first aspect of the present invention, there is provided an image conversion apparatus, comprising: a class tap extracting means for extracting a plurality of pixel data for generating a class code from a first image signal as a class tap; , Class classifying means for classifying a class tap to generate a class code representing the class, generating means for generating prediction data corresponding to the class code, and generating a second image signal using the prediction data. Generating means for detecting a feature amount indicating the degree of blur of the image of the first image signal, and detecting means for controlling a class tap extracting operation of the class tap extracting means in accordance with the detection result; It is characterized by the following.

[0006] The image conversion method according to the eleventh aspect is directed to the first method.
A plurality of pixel data for generating a class code is extracted as a class tap from the image signal of the image signal, a class code representing the class is generated by classifying the class tap, and prediction data corresponding to the class code is generated. Generating a second image signal using the prediction data;
The method is characterized in that a feature amount indicating a degree of blur of the image of the first image signal is detected, and a class tap extraction process is controlled in accordance with the detection result.

According to a twenty-first aspect of the present invention, there is provided an image conversion apparatus for converting a first image signal composed of a plurality of pixel data into a second image signal composed of a plurality of pixel data.
A plurality of pixel data for generating a class code is extracted as a class tap from the image signal of the image signal, a class code representing the class is generated by classifying the class tap, and prediction data corresponding to the class code is generated. Generating a second image signal using the prediction data;
Provided is a computer-readable program for detecting a feature amount indicating a degree of blur of an image of a first image signal and executing a process of controlling a class tap extraction process in accordance with the detection result. It is characterized by.

[0008] The image conversion apparatus according to claim 1, claim 1
In the image conversion method according to the first aspect and the providing medium according to the twenty-first aspect, a class tap is controlled in accordance with a feature amount representing a blur amount of input image data. As a result, even if the image quality of the input image data is poor, the optimal class tap can be extracted, and the optimal prediction processing can be performed.

[0009]

DESCRIPTION OF THE PREFERRED EMBODIMENTS Embodiments of the present invention will be described below. In order to clarify the correspondence between each means of the invention described in the claims and the following embodiments, each means is described. When the features of the present invention are described by adding the corresponding embodiment (however, an example) in parentheses after the parentheses, the result is as follows. However, of course, this description does not mean that each means is limited to those described.

According to a first aspect of the present invention, there is provided an image conversion apparatus, comprising: a class tap extracting means for extracting, as class taps, a plurality of pixel data for generating a class code from a first image signal (for example, an area shown in FIG. 1). A cutout unit 1) and a class classifying unit that classifies the class taps to generate a class code representing the class (for example, FIG. 1)
ADRC pattern extraction unit 4) and generating means (for example, R in FIG. 1) for generating prediction data corresponding to the class code.
OM table 6) and a generation unit that generates a second image signal using prediction data (for example, prediction calculation unit 7 in FIG. 1)
Detecting means for detecting a feature amount indicating a degree of blur of the image of the first image signal, and controlling a class tap extracting operation of the class tap extracting means in accordance with the detection result (for example, FIG. And a feature amount extraction unit 3).

According to a fifth aspect of the present invention, there is provided an image conversion apparatus for extracting a plurality of pixel data for performing a prediction operation from a first image signal as a prediction tap. Unit 2), wherein the generation means generates a set of prediction coefficients corresponding to the class code, and the generation means performs a prediction operation using the set of prediction taps from the prediction tap extraction means and the prediction coefficients from the generation means. Thus, a second image signal is generated.

An embodiment of the present invention will be described below. FIG. 1 is a block diagram illustrating a configuration example of an image conversion apparatus to which the present invention has been applied. In the figure, for example, SD image data (or HD image data) with poor image quality (of a blurred image) is converted into SD image data with improved image quality (or
HD image data). Hereinafter, a case where the input image data is SD image data will be described.

For example, SD with poor image quality (of a blurred image)
Image data is input to the image conversion device via the input terminal. The input image data is supplied to the region cutout unit 1, the region cutout unit 2, and the feature amount extraction unit 3.
The feature amount extraction unit 3 detects a feature amount representing a blur amount of the input SD image data, and outputs the detected feature amount to the region cutout unit 1, the region cutout unit 2, and the class code generation unit 5. The area cutout unit 1 cuts out a predetermined range of pixel data from the input image data as a set of class taps, and outputs this as an ADRC (Adaptive Dynam).
ic Range Coding) is output to the pattern extraction unit 4. The class tap cut out by the region cutout unit 1 is controlled in accordance with the feature amount output from the feature amount extraction unit 3.
The ADRC pattern extraction unit 4 performs a class classification for the purpose of expressing a waveform in a space.

The class code generator 5 is a class and feature quantity extractor 3 output from the ADRC pattern extractor 4.
And generates a class code corresponding to the feature quantity output from the ROM table 6. A predetermined set of prediction coefficients is stored in the ROM table 6 in advance corresponding to each class (class code), and the set of prediction coefficients corresponding to the class code is output to the prediction calculation unit 7.

The area cutout unit 2 cuts out a predetermined range of pixel data from the input image data as a set of prediction taps, and outputs the pixel data forming the prediction taps to the prediction calculation unit 7. The set of prediction taps cut out by the region cutout unit 2 is controlled in accordance with a feature amount representing a blur amount output from the feature amount extraction unit 3. The prediction calculation unit 7 performs a prediction calculation from the set of prediction taps input from the region cutout unit 2 and the set of prediction coefficients input from the ROM table 6, and converts the calculation result as image data with image quality corrected. Output. The output image data is displayed on, for example, a display device (not shown), recorded on a recording device, or transmitted by a transmission device.

Next, the operation will be described. When image data is input, the area cutout unit 1 executes a process of cutting out predetermined pixel data from the input image data as a class tap. For example, as shown in FIG. 2, a total of five pixel data including a predetermined target pixel data as a center and pixel data at a position corresponding to the target pixel data and pixel data adjacent vertically and horizontally are cut out as class taps. . Alternatively, as shown in FIG. 3, pixel data corresponding to the pixel data of interest is
Pixel data adjacent to the pixel-separated position is extracted as a class tap. What kind of pixel data is cut out as a class tap is determined in accordance with the feature amount representing the blur amount output from the feature amount extraction unit 3.

Here, the feature amount extraction processing of the feature amount extraction unit 3 will be described with reference to the flowchart of FIG. First, in step S1, the feature amount extraction unit 3
An autocorrelation coefficient for each frame for each input pixel data is calculated. Then, the autocorrelation coefficient is used as a measure of a feature amount representing a blur amount of the pixel data. That is,
As shown in FIG. 5, assuming that one frame of image data is composed of pixel data of 720 pixels × 480 pixels, a predetermined pixel of interest is 720 pixels × 480 pixels centered on the pixel of interest. 51 of the pixel data
A block composed of pixel data of 2 pixels × 256 pixels (hereinafter, this block is appropriately referred to as a reference block) is formed, and the position of the reference block is moved within a predetermined range in a pixel-by-pixel manner in the vertical and horizontal directions, An autocorrelation coefficient corresponding to each position when moved is calculated.

For example, each pixel value in a reference block centered on predetermined target pixel data is represented by X _ij (i = 0, 1, 2,
.., N, j = 0, 1, 2,..., M), the average value of the pixel values in the reference block is X _av , and each pixel value in the block corresponding to the position to which the reference block has been moved To Y _ij (i =
.., N, j = 0, 1, 2,.
m), assuming that the average value of the pixel values in the block is _Yav , the autocorrelation coefficient corresponding to the position when the reference block is moved is expressed by the following equation.

(Equation 1)

As described above, in this embodiment, since the reference block is composed of pixel data of 512 pixels × 256 pixels, the values are n = 511 and m = 255. In this way, it is possible to shift the reference block within the predetermined range and obtain the autocorrelation coefficient corresponding to each position.

FIG. 6 shows an example of the autocorrelation coefficient obtained in this way. When the block (reference block) is not shifted, the autocorrelation coefficient is 1. On the other hand, in the case of the frame F1, for example, when a block (reference block) is shifted rightward by three pixels, the autocorrelation coefficient decreases to 0.85, and as the shift amount increases, the autocorrelation coefficient decreases. The correlation coefficient drops to a smaller value. This is the same when the block (reference block) is shifted to the left.

On the other hand, in the frame F2, when the block (reference block) is shifted rightward or leftward by one pixel, the autocorrelation coefficient decreases to 0.85. Further decline. This means that the frame F1 has a stronger autocorrelation with the surroundings than the frame F2, that is, the frame F1
This means that the blur amount is larger than that of 2.

In step S2, the feature quantity extraction unit 3 determines that the autocorrelation coefficient is a predetermined reference value (for example, 0.85).
Is obtained, and in step S3, the pixel shift amount is output as a feature amount representing a blur amount. That is, the pixel shift amount at which the autocorrelation coefficient becomes the reference value is obtained by comparing the autocorrelation coefficient corresponding to each position and the reference value when the reference block is shifted within the predetermined range. In the example of FIG. 6, when the input pixel data is the pixel data of the frame F1, the feature amount is set to 3, and when the input pixel data is the pixel data of the frame F2, the feature amount is set to 1. You.

When the feature quantity 1 is input from the feature quantity extracting section 3, the area cutout section 1 cuts out pixel data arranged within a narrow interval as a class tap, for example, as shown in FIG. Do). On the other hand, when the feature amount 3 is input, the area cutout unit 1 cuts out (extracts) pixel data arranged at wider intervals as class taps, as shown in FIG.

As shown in FIG. 6, the range of pixel data having a strong autocorrelation is narrow in an image (frame F2) whose feature value is 1. Therefore, as shown in FIG.
Pixel data that is arranged in a narrow range is also selected as pixel data constituting a class tap. On the other hand, in the case of an image (frame F1) whose feature value is 3, the range having strong autocorrelation is wider. Therefore, as shown in FIG. 3, the pixel data constituting the class tap is also cut out from a wider range. As described above, by appropriately changing the pixel data to be cut out as a class tap in accordance with the feature amount representing the blur amount, a more appropriate class tap can be cut out.

Although not shown, the prediction taps in the region cutout unit 2 also correspond to the feature amounts representing the blur amounts output by the feature amount extraction unit 3 in the same manner as the cutout of the class taps in the region cutout unit 1. Pixel data cut out as a prediction tap is dynamically changed. Note that the prediction tap (pixel data) cut out by the region cutout unit 2 may be the same as or different from the class tap (pixel data) cut out by the region cutout unit 1.

The ADRC pattern extraction unit 4 performs ADRC processing on the class taps cut out by the area cutout unit 1 to perform class classification (determines the class).
That is, the dynamic range among the five pixel data extracted as class taps is DR, the bit allocation is n, the level of each pixel data as a class tap is L,
When the requantization code is Q, the following equation is calculated. Q = {(L−MIN + 0.5) × 2 ⁿ / DR} DR = MAX−MIN + 1

Here, {} means truncation processing. MAX and MIN represent the maximum value and the minimum value of the five pixel data constituting the class tap, respectively. As a result, for example, if it is assumed that each of the five pixel data forming the class tap cut out by the region cutout unit 1 is formed of, for example, 8 bits (n = 8), each of them is compressed into 2 bits. You. Therefore, data representing a space class represented by a total of 10 bits is supplied to the class code generator 5.

The class code generator 5 converts the data representing the space class input from the ADRC pattern extractor 4 into:
A class code is generated by adding a bit representing a feature amount representing a blur amount supplied from the feature amount extraction unit 3. For example,
Assuming that the feature amount representing the blur amount is represented by 2 bits, 1
A 2-bit class code is generated and the ROM table 6
Supplied to This class code is stored in the ROM table 6
Address.

In the ROM table 6, a set of prediction coefficients corresponding to each class (class code) is stored at an address corresponding to the class code, and based on the class code supplied from the class code generation unit 5. ,
The prediction coefficient sets ω _{1 to} ω _n stored at the address corresponding to the class code are read out and supplied to the prediction calculation unit 7.

The prediction calculation unit 7 is configured to calculate the pixel data x _{1 to} x x constituting the prediction tap supplied from the region cutout unit 2.
_The prediction result y is calculated by performing a product-sum operation on _n and the prediction coefficients ω _{1 to} ω _n as shown in the following equation. y = ω ₁ x ₁ + ω ₂ x ₂ + ... + ω _n x _n

The predicted value y becomes pixel data whose image quality (blur) has been corrected.

FIG. 7 shows an example of another feature amount extraction process in the feature amount extraction unit 3. In this example, in step S11, an edge near a predetermined target pixel is detected. In step S12, an edge code corresponding to the detected edge is output as a feature value. For example, when an oblique edge is detected from the upper right to the lower left as shown in FIG. 8, the feature amount extraction unit 3 outputs an edge code 0, and as shown in FIG. If detected, edge code 1 is output.

When the edge code 0 shown in FIG. 8 is input from the feature amount extraction unit 3,
Pixel data such as 0 is cut out (extracted) as a class tap. This class tap is configured by pixel data that is optimal for detecting an edge extending from the upper right to the lower left. On the other hand, the region cutout unit 1
When edge code 1 as shown in FIG. 9 is input, pixel data as shown in FIG. 11 is cut out (extracted) as a class tap. This class tap is composed of pixel data that is optimal for detecting a horizontal edge. Similarly, in the region cutout unit 2, a process of cutting out (extracting) pixel data forming a prediction tap corresponding to the edge code is executed.

As described above, it is possible to dynamically change the pixel data to be cut out as a class tap or a prediction tap that is cut out in accordance with the feature amount such as the autocorrelation or the edge of the input pixel data, so that a more appropriate prediction operation can be performed. The result can be obtained.

FIG. 12 shows a configuration example for obtaining a set of prediction coefficients for each class (each class code) stored in the ROM table 6 by learning. In this configuration example, for example, a configuration is used in which a set of prediction coefficients for each class (each class code) is generated using SD image data (or HD image data) as a teacher signal (learning signal) with good image quality. It is shown. The configuration example described below is an example for generating a set of prediction coefficients for each class corresponding to the image conversion apparatus in FIG. 1 according to the present embodiment.

For example, image data as a teacher signal (learning signal) with good image quality is input to the normal equation calculation unit 27 and also to the low-pass filter (LPF) 21. The low-pass filter 21 generates image data (learning signal) having deteriorated image quality by removing low-frequency components of the image data as the input teacher signal (learning signal). The image data (learning signal) having deteriorated image quality output from the low-pass filter 21 is used as a region tap for extracting (extracting) a predetermined range of image data as a class tap, and a predetermined range of image data as a prediction tap. An area extracting unit 23 for extracting (extracting) a feature, and a feature extracting unit 24 for extracting a feature representing a blur amount
Is input to The feature amount extracting unit 24 extracts a feature amount representing a blur amount of the pixel data of the input image data (learning signal) having deteriorated image quality, and extracts the extracted feature amount from the region cutout unit 22 and the region cutout unit 23. , And the class code generator 26. The region cutout unit 22 and the region cutout unit 23 dynamically change pixel data to be cut out as a class tap or a prediction tap in accordance with the input feature amount representing the blur amount.

The ADRC pattern extraction unit 25 classifies the pixel data as the class tap input from the region extraction unit 22 (determines the class), and outputs the classification result to the class code generation unit 26. The class code generation unit 26 generates a class code from the classified class and the feature amount representing the blur amount, and generates a normal equation calculation unit 27
Output to The configuration and operation of each of the above-described region cutout unit 22, region cutout unit 23, feature amount extraction unit 24, ADRC pattern extraction unit 25, and class code generation unit 26 are the same as those of region cutout unit 1 shown in FIG.
Since they are the same as the area cutout unit 2, the feature amount extraction unit 3, the ADRC pattern extraction unit 4, and the class code generation unit 6, the description is omitted here.

The normal equation calculation section 27 generates a normal equation for each class (for each class code) from the input teacher signal (learning signal) and the pixel data as the prediction tap supplied from the area cutout section 23. Is supplied to the prediction coefficient determination unit 28. Then, when the required number of normal equations is obtained for each class, the normal equation calculation unit 27 solves the normal equation using the least square method for each class, and calculates a set of prediction coefficients for each class. . The calculated set of prediction coefficients for each class is supplied from the prediction coefficient determination unit 28 to the memory 29, and the memory 2
9 is stored. The set of prediction coefficients for each class stored in the memory 29 is written to the ROM table 6 in FIG.

In the example described above, the set of prediction coefficients for each class is calculated and obtained by the configuration shown in FIG. 12, but it may be calculated and calculated using a computer.

Further, in the present embodiment, a set of prediction coefficients for each class, which is stored in the ROM table 6 shown in FIG. 1 and calculated by the method shown in FIG. Although pixel data with improved image quality (improved blur) is generated from pixel data, the present invention is not limited to this. Pixel data for each class (for each class code) calculated by learning is stored in the ROM table 6. The predicted value itself of the data may be stored, and the predicted value may be read by a class code.

In this case, the region cutout unit 2 shown in FIG. 1 and the region cutout unit 23 shown in FIG. 12 can be omitted, and the prediction calculation unit 7 shown in FIG.
Is converted into a format corresponding to the output device and output. Further, in this case, the normal equation operation unit 2 shown in FIG.
Instead of 7 and the prediction coefficient determination unit 28, a predicted value for each class is generated using the centroid method, and the predicted value for each class is stored in the memory 29.

Further, instead of the predicted value itself for each class, each predicted value for each class is normalized by a reference value, and the normalized predicted value for each class is stored in the ROM table 6.
May be stored. In this case, the prediction calculation unit 7 shown in FIG. 1 calculates a prediction value from a prediction value normalized based on a reference value.

Further, in the present embodiment, the number of pixel data cut out as class taps or prediction taps is five when the autocorrelation coefficient is used, and seven or eight when the edge code is obtained. However, the present invention is not limited to this, and the number of pieces of pixel data cut out as class taps or prediction taps may be any number. However, as the number of class taps or prediction taps increases, the accuracy of image quality improvement increases. However, since the amount of calculation increases and the memory increases, the amount of calculation increases.
Since the load on hardware increases, it is necessary to set an optimal number.

Further, in the present embodiment, conversion from an SD image signal to an SD image signal (SD-SD conversion), H
Although the conversion from a D image signal to an HD image signal (HD-HD conversion) is described, the present invention is not limited to this, and can be applied to conversion of other formats (interlace signal, non-interlace signal, etc.). It is. Also, conversion from an SD image signal to an HD image signal (S
The present invention is also applicable to conversion between different formats, such as D-HD conversion) or conversion from an interlace signal to a non-interlace signal (inter-non-inter conversion). However, in this case, when the image data is cut out as the class tap or the prediction tap, since the pixel serving as the target pixel data does not actually exist, it does not become the target pixel data of the cutout.

Various modifications and application examples can be considered without departing from the gist of the present invention. Therefore,
The gist of the present invention is not limited to the present embodiment.

As a providing medium for providing a user with a computer program for performing the above processing,
In addition to recording media such as magnetic disks, CD-ROMs, and solid-state memories, communication media such as networks and satellites can be used.

[0047]

As described above, according to the image conversion apparatus according to the first aspect, the image conversion method according to the eleventh aspect, and the providing medium according to the twenty-first aspect, the blur amount of the input image data. Since the cutout of the class tap or the prediction tap is controlled in accordance with the feature amount representing the, it is possible to extract the optimal pixel data as the class tap or the prediction tap even if the image quality of the input image data is poor. And it is possible to perform appropriate prediction processing.

[Brief description of the drawings]

FIG. 1 is a block diagram illustrating a configuration example of an image conversion device to which the present invention has been applied.

FIG. 2 is a diagram illustrating a cutout process in an area cutout unit 1 of FIG. 1;

FIG. 3 is a diagram illustrating a cutout process in an area cutout unit 1 of FIG. 1;

FIG. 4 is a flowchart illustrating a feature amount extraction process in a feature amount extraction unit 3 of FIG. 1;

FIG. 5 is a diagram illustrating a process of calculating an autocorrelation coefficient in step S1 of FIG. 4;

FIG. 6 is a diagram illustrating an autocorrelation coefficient calculated in step S1 of FIG.

FIG. 7 is a diagram illustrating another feature amount detection process in the feature amount extraction unit 3 of FIG. 1;

FIG. 8 is a diagram showing another example of feature amount detection in the feature amount extraction unit 3 of FIG. 1;

FIG. 9 is a diagram illustrating an example of another feature amount detection in the feature amount extraction unit 3 in FIG. 1;

FIG. 10 is a diagram illustrating a cutout process in the region cutout unit 1 of FIG. 1;

FIG. 11 is a diagram illustrating a cutout process in the region cutout unit 1 of FIG. 1;

12 is a block diagram showing a configuration example for performing a learning process of a prediction coefficient in the ROM table 6 of FIG. 1;

[Explanation of symbols]

1, 2 area extraction unit, 3 feature amount extraction unit, 4
ADRC pattern extraction unit, 5 class code generation unit,
6 ROM table, 7 prediction operation unit

 ──────────────────────────────────────────────────続 き Continuing on the front page (72) Inventor Takaya Hoshino 6-7-35 Kita-Shinagawa, Shinagawa-ku, Tokyo Inside Sony Corporation (72) Inventor Masaaki Hattori 6-35, Kita-Shinagawa, Shinagawa-ku, Tokyo Sony Corporation

Claims (21)

[Claims] 1. An image conversion apparatus for converting a first image signal composed of a plurality of pixel data into a second image signal composed of a plurality of pixel data, wherein a class code is generated from the first image signal. Class tap extracting means for extracting a plurality of pixel data as class taps, class classifying means for classifying the class tap to generate a class code representing the class, and predicting data corresponding to the class code. Generating means for generating; generating means for generating the second image signal using the prediction data; and detecting a feature amount representing a degree of blur of the image of the first image signal, Correspondingly, an image conversion device comprising a detecting means for controlling the class tap extracting operation of the class tap extracting means. 2. The image conversion device according to claim 1, wherein the second image signal is a signal whose image quality has been improved from the first image signal. 3. The image conversion device according to claim 1, wherein the first image signal and the second image signal have the same format. 4. The generation means has a memory for storing prediction data generated by learning in advance for each class using a learning signal having good image quality, and the memory stores the prediction data using the class code as an address. The image conversion apparatus according to claim 1, wherein 5. A prediction tap extracting unit for extracting a plurality of pixel data for performing a prediction operation from the first image signal as a prediction tap, wherein the generation unit includes a prediction coefficient corresponding to a class code. Wherein the generation means generates the second image signal by performing a prediction operation using a prediction tap from the prediction tap extraction means and a set of prediction coefficients from the generation means. The image conversion device according to claim 1. 6. The generating means has a memory for storing a set of prediction coefficients generated by learning in advance for each class using a learning signal with good image quality, wherein the memory uses the class code as an address. The image conversion apparatus according to claim 5, wherein a set of prediction coefficients is output. 7. The detecting means calculates an autocorrelation coefficient corresponding to each shifted position by shifting pixel data in a predetermined range of the image signal, and calculates the autocorrelation coefficient in the image signal. The image conversion apparatus according to claim 1, wherein a feature amount indicating a degree of blur of the image is detected as a scale indicating a degree of blur. 8. The method according to claim 1, wherein the detecting means determines the autocorrelation coefficient corresponding to a reference value, and indicates a shift amount of pixel data corresponding to the determined autocorrelation coefficient to indicate a degree of blur of the image. 8. The output as a quantity.
An image conversion device according to claim 1. 9. The image conversion apparatus according to claim 7, wherein the autocorrelation coefficient is a scale indicating a degree of blurring of the image. 10. The class classifying means classifies the class tap into a first class representing the class.
2. The image conversion apparatus according to claim 1, wherein a class code including a class code of the first class code and a second class code indicating a feature amount indicating a degree of blur of the image is generated. 11. An image conversion method for converting a first image signal composed of a plurality of pixel data into a second image signal composed of a plurality of pixel data, wherein a class code is generated from the first image signal. A plurality of pixel data for the class tap as a class tap, classifying the class tap into a class, generating a class code representing the class, generating prediction data corresponding to the class code, and using the prediction data. Generating the second image signal, detecting a feature amount indicating the degree of blur of the image of the first image signal, and controlling the class tap extraction process in accordance with the detection result. Characteristic image conversion method. 12. The image conversion method according to claim 11, wherein the second image signal is a signal whose image quality has been improved from the first image signal. 13. The image conversion method according to claim 11, wherein the first image signal and the second image signal have the same format. 14. The step of generating the prediction data comprises the steps of:
12. The image conversion method according to claim 11, wherein prediction data generated by learning in advance for each class is generated by using a learning signal with good image quality stored therein. 15. A step of further extracting, as prediction taps, a plurality of pixel data for performing a prediction operation from the first image signal, and generating the prediction data includes setting a prediction coefficient corresponding to a class code. Generating the second image signal by generating a second image signal by performing a prediction operation using the set of the prediction taps and the prediction coefficients in the step of outputting the second image signal. The image conversion method described in 1. 16. In the step of generating a set of prediction coefficients, the class code is generated by learning in advance for each class by using the class code as an address and using a learning signal of good image quality stored therein from a memory. 16. The method according to claim 15, wherein a set of prediction coefficients is generated. 17. The step of detecting a feature value comprises:
By shifting the pixel data in a predetermined range of the image signal, an autocorrelation coefficient corresponding to each shifted position is calculated, and the autocorrelation coefficient is used as a scale indicating the degree of blur of the image, and 12. The image conversion method according to claim 11, wherein a feature amount indicating a degree of blur is detected. 18. The method according to claim 18, wherein the detecting the characteristic amount comprises:
Determining an autocorrelation coefficient corresponding to a reference value, and outputting a shift amount of pixel data corresponding to the determined autocorrelation coefficient as a feature amount indicating a degree of blur of the image. Item 18. The image conversion method according to Item 17. 19. The image conversion method according to claim 17, wherein the autocorrelation coefficient represents a degree of blur of the image. 20. In the step of generating the class code, a first class code representing the class by classifying the class tap and a second class representing a feature amount representing a degree of blur of the image. 12. The image conversion method according to claim 11, wherein a class code including a code is generated. 21. An image conversion device for converting a first image signal composed of a plurality of pixel data into a second image signal composed of a plurality of pixel data, wherein a class code is generated from the first image signal. A plurality of pixel data for the class tap as a class tap, classifying the class tap into a class, generating a class code representing the class, generating prediction data corresponding to the class code, and using the prediction data. A process of generating the second image signal, detecting a feature amount indicating a degree of blur of the image of the first image signal, and controlling the class tap extraction process in accordance with the detection result. A providing medium for providing a computer-readable program to be executed.