JPH06164939A - Encoder for picture signal - Google Patents

Abstract

PURPOSE:To realize the adaptive encoding with a high efficiency and less in picture quality deterioration by employing the waveform analysis method in a picture element space analyzing a waveform of each input block based on a direction of a gradation change and a characteristic in the amplitude direction. CONSTITUTION:A picture signal is divided into an input block comprising mXn picture elements by a block extract section 2 and an orthogonal transformation section 100 applies orthogonal transformation to the input block to obtain a transformation coefficient. An area analysis section 7 analyzes a waveform of the input block. A quantization matrix storage section 104 stores plural sets of quantization matrices suitable for each waveform in advance and a corresponding quantization matrix is read in response to the result of analysis of waveform by an area analysis section 7. A quantization section 102 quantizes the transformation coefficient with a quantization matrix read from the quantization matrix storage section 104 to obtain a quantization coefficient. A variable length coding means 106 applies variable length encoding to the quantization coefficient and a multiplexer section 108 multiplexes the encoding result to obtain code data.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an image signal coding apparatus.

[0002]

2. Description of the Related Art As an image signal encoding method, a JPEG method (ISO-I
EC / CD 10918-1, "Digital Co
compression and Coding of C
ongoing-toneStill Image
s Part 1 Requirements andg
Discrete Cosine Transform (Discrete Transform), which is a type of orthogonal transform, such as that used in the
A method based on e Cosine Transform is known. For example, the transformation of the 8th-order two-dimensional discrete cosine transformation is given by the equation (1), and the inverse transformation becomes the equation (2).

[0003]

[Equation 1] here,

[Equation 2] Also, f (i, j) represents each element of the pixel block,
i and j represent the position of the element. F (u, v) represents each element of the transform coefficient, and u, v represents the position of the element.

It is known that in an image signal called a natural image of a person, a landscape or the like, adjacent pixels tend to have pixel values close to each other, and have a high correlation. Such a highly correlated signal means that the signal power is concentratedly distributed on a specific frequency component when viewed on the frequency axis. If only the coefficient of the component in which the signal power is concentrated and distributed is encoded, it is possible to reduce the information amount as a whole. In the case of a natural image, most of the signal power is concentrated in the low frequency region by performing the discrete cosine transform.

The configuration of the conventional example will be described below with reference to FIG.

In the figure, 1 is an input image, 3 is an input block cut out from the input image by the block extraction unit 2, 101 is an input block 3 by the orthogonal transformation unit 100.
Is a transform coefficient obtained by performing the discrete cosine transform shown in the equation (1), 103 is a quantization matrix stored in the quantization matrix storage unit 104, and 105 is a quantization matrix 103 that transforms the transform coefficient 101 into a quantization matrix 103. Quantized coefficient obtained by quantizing, 107 is a variable length code obtained by variable length encoding the quantized coefficient 105, 1
Reference numeral 09 is code data in which the variable length code 107 is multiplexed. Further, 2 is a block extraction unit that extracts an input block 3 that is a rectangular region of pixels from the input image 1, 100 is an orthogonal transformation unit that performs a discrete cosine transform on the input block 3, and 104 is a quantum that stores a quantization matrix. A quantization matrix storage unit 102, a quantization unit 106 that quantizes the transform coefficient 101 using the quantization matrix 103,
Is a variable length coding unit for variable length coding the quantized coefficient 106, and 108 is a multiplexing unit for multiplexing the variable length code 107 to form code data 109.

The operation will be described below with reference to FIG.

The block extracting section 2 extracts an input block 3 which is a rectangular area of pixels from the input image 1 as shown in FIG. The figure shows the case of the area of 8 × 8 pixels,
In the following example, a size of 8 × 8 pixels will be described.

Then, in the orthogonal transform unit 100, the discrete cosine transform shown in the equation (1) is applied to the input block 3. As a result of the discrete cosine transform, the transform coefficient 101
Is obtained as a matrix of size 8 × 8. At this time, the transform coefficient 101 is output as a one-dimensional coefficient string in which the matrix is zigzag scanned as shown in FIG.

The quantization processing in the quantization unit 102 is performed using the transform coefficient 101 and the quantization matrix 103 stored in the quantization matrix storage unit 104. Quantization is a rounding process defined by the following equation.

C (u, v) = (F (u, v) + (Q (u, v) / 2)) / Q (u, v) (F (u, v) ≧ 0) ... (4) C (u, v) = (F (u, v)-(Q (u, v) / 2)) / Q (u, v) (F (u, v) <0) ... (5) Here, F (u, v) and Q (u, v) represent the transform coefficient and each element of the quantization matrix, respectively. u and v represent the position of the element. FIG. 13 shows an example of the quantization matrix 103. Hereinafter, each value for each coefficient position is called a quantization step value.

The quantization matrix 103 is stored in the quantization matrix storage unit 104 in the zigzag scan order shown in FIG. As a result, the quantization unit 102 reads the quantization step value at the position corresponding to the transform coefficient 101 in the zigzag scan order, and (4),
The quantization process of the equation (5) is sequentially executed.

In the conventional example, the image quality and the coding efficiency are determined by the quantization process. The information reduction effect in encoding is realized by reducing the bit precision of transform coefficients. Normally, the power distribution of the conversion coefficient is biased, so
By setting the bit precision of the coefficient on which the power is concentrated and the bit precision of the coefficient on which the power is not concentrated roughly, the reproduction image quality and the coding efficiency are compatible with each other. In the quantization matrix shown in FIG. 13, many bits are allocated to the coefficient of the low frequency component, and few bits are allocated to the coefficient of the high frequency component.

However, the above-mentioned JPEG method does not have a method of analyzing the waveform of an input image, and one kind of quantization is performed for one image (in the case of a color image, each color component). Since only the characteristics can be used, there is a problem in that it cannot be adapted to the content of the original document, and it is difficult to sufficiently achieve both the reproduction image quality and the improvement of the coding efficiency.

To solve this problem, a method of improving both image quality and compression efficiency by determining the optimal bit distribution based on the variance of each transform coefficient in accordance with the characteristics of each image is as follows. Morio Onoe, "Image Processing Handbook (9.3 Encoding of still image of grayscale)", published by Shokoido Co., Ltd., 1987, p221. The distribution of the number of bits in this adaptive encoding can be expressed as the following equation.

[0016]

[Equation 3] Here, b (u, v) is the number of bits assigned to the transform coefficient F (u, v), σ (u, v) ² is the variance of the transform coefficient F (u, v), and θ is the average number of bits. .

As a result, the number of allocated bits b (u,
v) and the dynamic range L (u, v) of the transform coefficient, the quantization step value Q (u, v) can be determined by the following equation.

Q (u, v) = Int [L (u, v) / 2 ^{b (u, v)} ] (8) Here, Int [] means to be an integer.

In the variable length coding unit 106 shown in FIG.
Variable length coding such as Huffman coding is performed on the quantized coefficient 105, and the assigned variable length code 107 is output.

The multiplexer 108 completes the encoding operation by multiplexing the variable length codes to form the code data 109.

[0021]

Normally, it is expected that different types of regions such as characters and photographs will coexist in an image input by a scanner or the like. When transform coding is applied to such different regions, the power distribution of transform coefficients greatly differs depending on the regions.

The technique itself for performing the optimal bit allocation according to the characteristics of the image is disclosed in the above-mentioned "Image Processing Handbook", but in the conventional technique disclosed in this document, the entire image to be encoded is Since the bit allocation was determined based on the average characteristics, the difference in characteristics between different areas was not considered. Therefore, even when the photographic image includes a character portion, the average bit allocation calculated for the entire image is applied. In the character part, a lot of bit allocation is required to save the high frequency component generated by the edge,
Furthermore, the distribution of high frequency components differs depending on the direction of the edge. There is a problem in that the difference in these characteristics cannot be dealt with only by the average bit allocation, and the character image quality is deteriorated.

14 to 16 are pixel blocks having gradation changes in the horizontal direction (FIG. 14), pixel blocks having gradation changes in the vertical direction (FIG. 15), and pixel blocks having gradation changes in the diagonal direction. FIG. 16 shows the correspondence between the pixel distribution (FIGS. 14A to 16A) and the conversion coefficient power distribution (FIGS. 14B to 16B) for each of FIGS. As can be seen from FIGS. 14 to 16, the distribution of coefficient power differs depending on the direction of gradation change and the magnitude of amplitude change in the input block. Therefore, in the case of transform coding, adaptive coding that determines the quantization characteristic of transform coefficients by analyzing the waveform of the input block is considered promising from the viewpoint of image quality and efficiency.

According to the present invention, the adaptive coding with high efficiency and little deterioration in image quality is achieved by using the waveform analysis method in the pixel space for analyzing the waveform of each input block from the characteristics of the gradation change direction and the amplitude direction. The purpose is to realize.

[0025]

In the image signal coding apparatus of the present invention, the image signal is composed of a plurality of pixels m ×
Block extraction means for dividing the input block into n pixels (m and n are positive integers), orthogonal transformation means for subjecting the input block to orthogonal transformation to obtain transformation coefficients, and quantization characteristic storage for storing the characteristics of quantization. Means, quantizing means for quantizing the transform coefficient with a characteristic stored in the quantizing characteristic storing means to obtain a quantized coefficient, coding means for variable-length coding the quantized coefficient, and variable-length coding The image signal coding apparatus having a multiplexing unit for multiplexing the result of (1) to obtain coded data is provided with a waveform analysis unit for analyzing the waveform of the input block.

Further, in the waveform analysis means, an average value separating means for subtracting an average value from each pixel in the input block, and a direction of gradation change of the average value separating block obtained by the average value separating means The first to analyze the feature quantity
Analyzing means, second analyzing means for analyzing the feature value in the amplitude direction of the average value separation block, and the input block based on the analysis results of the first analyzing means and the second analyzing means. It is characterized in that it is provided with a judging means for judging the characteristics of the waveform.

[0027]

The block extracting means divides the image signal into input blocks of m × n pixels (m and n are positive integers) each consisting of a plurality of pixels. The orthogonal transform means performs orthogonal transform on the input block to obtain transform coefficients. The waveform analysis means analyzes the waveform of the input block. The quantization characteristic storage means sets the quantization characteristic corresponding to the waveform analysis result. The quantizing means quantizes the transform coefficient with a characteristic set in the quantizing characteristic storing means to obtain a quantized coefficient. The coding means performs variable length coding on the quantized coefficient, and the multiplexing means multiplexes the coding result to obtain coded data.

[0028]

FIG. 1 is a diagram showing the configuration of an embodiment of the present invention. In the figure, parts corresponding to those of the conventional example of FIG. 10 are designated by the same reference numerals.

In the figure, 1 is an input image, 3 is an input block cut out from the input image by the block extracting unit 2, 13 is an input block 3 by an area analyzing unit 7 which will be described later.
Area information which is the analysis result of the orthogonal transformation unit 100
Is a transform coefficient obtained by subjecting the input block 3 to the discrete cosine transform shown in equation (1), and 103 is a quantization matrix selected by the region information 13 from the set of quantization matrices stored in the quantization matrix storage unit 104. , 105 is a quantized coefficient obtained by quantizing the transform coefficient 101 with the quantization matrix 103 by the quantizer 102, 107 is a variable length code obtained by variable length encoding the quantized coefficient 105, and 109 is a variable length Code 10
7 is coded data. Further, 2 is a block extraction unit that extracts an input block 3 that is a rectangular region of pixels from the input image 1, 7 is an area analysis unit that analyzes the waveform and gain of the input block 3, and outputs the result as region information 13. Reference numeral 100 denotes an orthogonal transform unit that performs a discrete cosine transform on the input block 3, 104 denotes a quantization matrix, and the quantization matrix 1 corresponding to the area information 13 is stored.
03 is a quantization matrix storage unit, 102 is a quantization unit that quantizes the transform coefficient 101 using the quantization matrix 103, and 106 is a variable length coding unit that performs variable length coding on the quantized coefficient 105. , 108 is a variable length code 10
7 is a multiplexing unit that multiplexes 7 to form coded data 109.

FIG. 2 is a diagram for explaining the structure of the area analysis unit 7 shown in FIG.

In the figure, 3 is an input block of m × n pixels (m and n are positive integers) cut out by the block extraction unit 2, and 6 is an average value separation unit 4 which calculates the average value of the input block 3 from each pixel. The subtracted average value separation block, 18 is waveform information that is the result of analyzing the waveform of the average value separation block 6 by the waveform analysis unit 14, and 27 is the result of analyzing the gain of the average value separation block 6 by the gain analysis unit 20. Gain information 13 is area information which is a result of area determination based on the waveform information 18 and the gain information 27 in the area determination unit 12. Also, 4 calculates the average value of the input block 3 and subtracts the average value from each pixel to obtain the average value separation block 6
Is a mean value separation unit, 14 is a waveform analysis unit that analyzes the waveform of the mean value separation block 6 and outputs waveform information 18, 20
Is a gain analysis unit which analyzes the gain information of the average value separation block 6 and outputs the gain information 27, and 12 is a region determination which determines the region of the input block 3 from the waveform information 18 and the gain information 27 and outputs the region information 13. It is a department.

FIG. 3 is a block diagram of the waveform analyzer 14 shown in FIG. In the figure, 16 is a shape index representing the representative vector selected by the shape analysis unit 15, and 18 is a shape index.
Is the waveform information output from the waveform mapping table 17. Further, 15 is a shape analysis for performing pattern matching between a set of representative vectors having representative waveform information and an average value separation block 6 which is a block consisting of m × n pixels, and selecting a representative vector having the closest waveform information. Department,
Reference numeral 17 is a waveform mapping table for obtaining the waveform information 18 from the shape index 16.

As shown in FIG. 4, the shape analysis unit 15 in FIG. 3 sets a representative vector set having typical waveform information prepared in advance and an analysis target block (hereinafter referred to as an analysis block), that is, an average value. Waveform information analysis is performed by pattern matching with the separation block 6. By the waveform information analysis, the shape index 16 is obtained as the direction of the gradation change of the analysis block.

The analysis block of m × n pixels is x = {x _i │
i = 1, 2 ,. ． ． , M × n}, a representative vector set consisting of k representative vectors is Y = {y _i | i = 1,
2 ,. ． ． , K}, the pattern matching can be defined by the following equation.

D (x, y _p ) = min {d (x, y _i )} (for all i) (i = 1, 2, ..., k) where d (x, y _i ) is a distortion measure between x and y _i, and is defined by square distortion or the like. p is the index of the representative vector, that is, the shape index 16, and indicates that the representative vector y _p represented by _p is selected as the representative vector having the waveform information closest to the analysis block.

The operation relating to the waveform analysis will be described below.

The set of representative vectors is designed by performing a principal component analysis on gradation changes having horizontal, vertical, and other directions. In order to reduce the memory for storing the set of representative vectors, pattern matching is performed by dividing it into partial blocks. For example, the input block 3 is 8
If the size is × 8, pattern matching is performed for every four partial blocks of 4 × 4 pixels. The four shape indexes obtained for each partial block represent the characteristics of the two-dimensional waveform of the 4 × 4 pixel block in the input block of 8 × 8 pixels. These four indexes are
Waveform information 1 is mapped in the waveform mapping table 17 to information indicating the waveform of the entire 8 × 8 pixel block.
It is output as 8. At this time, variations (complexity) in the waveform directions of the four partial blocks are taken into consideration. For example,
The complexity is low if the directions of the waveforms of the four partial blocks are all the same, and the complexity is high if the directions of the waveforms of the four partial blocks are all different.

FIG. 5 is a block diagram of the gain analyzer 20 shown in FIG. In the figure, 23 is a variance value output from the variance calculator 22, 25 is histogram information output from the histogram counter 24, 27 is gain information, and 31 is a dynamic ratio which is a ratio between the maximum value and the minimum value in the average value separation block 6. It is a range ratio. 22 is a variance calculator that calculates the variance of the m × n pixel values of the average value separation block 6, 24 is a histogram counter that counts the frequency distribution of the m × n pixel values of the average value separation block 6, and 30 is A maximum / minimum detector that detects the maximum value and the minimum value in the average value separation block 6 and calculates the ratio of the maximum value and the minimum value, 26 is a variance value 23, histogram information 25, and dynamic range ratio 31 to gain information 27. Is a gain mapping table for obtaining

The operation relating to the gain analysis will be described below.

The gain analysis unit 20 of FIG. 5 analyzes the amplitude of the average value separation block 6, the frequency distribution of pixel values, and the ratio of the maximum value and the minimum value. From the results, the input block 3 is a block of the character part. It is determined whether there is a block in the photograph area. The gain information analysis is performed by calculating the variance value σ ² of the values of m × n pixels forming the average value separation block 6, counting the histogram, and the ratio between the maximum value and the minimum value.

The variance calculator 22 of FIG. 5 calculates the variance value σ ² of the values of m × n pixels forming the average value separation block 6, that is, the variance value 23. The variance value of m × n pixels obtained by separating the average value is defined by the following equation.

[0042]

[Equation 4] Alternatively,

[Equation 5] Here, the description will be given below using the variance value σ.

As shown in FIG. 6, the histogram counter 24 of FIG. 5 thresholds the average value separation block 6 with the variance value σ to count the frequency. That is, the threshold is ± σ / a
The frequency is counted at three points in the range of less than −σ / a, greater than −σ / a and less than or equal to σ / a, and greater than σ / a.
Here, a is a positive real number that is not 0, and in this embodiment, for example, a = 3. Frequency values counted in three places each H _-1, and H _0, H _1. As shown in FIG. 6, H _-1 , H
_{From 0} and H ₁ , it is determined whether the histogram is a unimodal distribution (see FIG. 11A) or a bimodal distribution (see FIG. 11B), and the result is obtained as histogram information 25. For example, H _-1
If ≦ H ₀ and H ₀ ≧ H ₁ , it is determined that the distribution has a single peak, and in other cases, the distribution has a double peak.

Generally, in the case of a character area, peaks of the histogram appear at the positions corresponding to the character color and the background color, and therefore, in the case of the bimodal distribution, it can be determined as a character area.

In the maximum / minimum detector 30, the dynamic range ratio r, which is the ratio of the maximum value and the minimum value, is calculated based on the following equation.

R = max {x _ij } / min {x _ij }, (i = 1, ...
., M, j = 1, ..., N) (11) In the character area, when part of the edge of the character or the like is applied to the block boundary, the number of pixels of the background color and the character color Since the number of pixels of is significantly different, the determination may be erroneous depending on the variance and the histogram. By introducing the dynamic range ratio r, when r is large, it is determined that a part of an edge such as a character overlaps the block boundary.
As a result, it is possible to avoid an erroneous determination as to the photograph area.

The gain mapping table 26 of FIG.
From the variance value 23, the histogram information 25, and the dynamic range ratio 31, the gain information 27, which is the identification result of the character area and the photograph area, is obtained.

FIG. 7 shows an example of a tree structure for area determination in the gain mapping table 26. In each section, threshold processing is performed on the histogram information 25, the variance value 23, and the dynamic range ratio 31, and branch determination is performed. That is, when the histogram shows a bimodal distribution, it is determined to be a character, and when it is determined that the histogram has a unimodal distribution and the intra-block variance is large, it is determined to be a photographic region. When the histogram has a unimodal distribution and it is determined that the variance within the block is small, the character and the photo area are further distinguished by the ratio of the dynamic range.
The threshold value in each section, which is the criterion, is set for the characteristics of the input image. Note that, here, the case where each of the histogram information 25, the variance value 23, and the dynamic range ratio 31 is determined by a single threshold value has been described, but the number of threshold values is not limited to this. Therefore,
The number of branches in each clause is not limited to two, and it is possible to construct a tree structure having more branches.

Gain information 27 obtained by the gain analysis unit 20
Is supplied to the area determination unit 12 of FIG. 2 together with the waveform information 18 from the waveform analysis unit 14 described above.

The area determination unit 12 determines the area information 13 from the waveform information 18 and the gain information 27 which are the results of the above-mentioned waveform analysis and gain analysis.

The area information 13 is composed of information indicating the distinction between the character area and the photograph area, and information indicating the direction of gradation change in each area.

Next, the adaptive coding based on the area information 13 will be described.

In the embodiment shown in FIG. 1, the quantization matrix 13 is switched by the area information 13 obtained by the procedure described above. This is performed in the quantization matrix storage unit 104.

The quantization matrix stored in the quantization matrix storage unit 104 needs to be created in advance. For this, for example, the method disclosed in the "Image processing handbook" described in the conventional example may be applied to each separated area.

In FIG. 8, the photographic area and the character area are separated from each other on the basis of the characteristics in the amplitude direction with respect to the actual image, and the character area is further vertical, horizontal, diagonal, and other in the direction of gradation change. An example of separating into four ways will be shown. FIG. 9 corresponds to FIG.
5 is an example of the quantization matrix of 5 types designed for each area divided into the photograph area and the four character areas as described above. FIG. 9A corresponds to a photograph area, and FIGS. 9B, 9C, 9D, and 9E show character areas having gradation changes in vertical, horizontal, diagonal, and other directions, respectively. It corresponds. Here, the quantization matrix 13 is designed so that the total number of bits distributed per block becomes equal. Further, the quantization step values for direct current are set equal to each other.

In this way, by determining the quantization matrix (quantization characteristic) corresponding to the area in advance, the quantization characteristic is switched according to the area determination result of the input block when actually encoding. An adaptive coding method can be realized.

The procedure of variable length coding after quantization is the same as that of the prior art, and will be omitted.

Since information on this adaptation is required on the decoding side, the area information 13 is multiplexed on the coded data 109 in the multiplexing section 108 in FIG. In the case of eight types of adaptation, 3 bits may be added per block for identification.

In the above, in the embodiment, the method of changing the quantization matrix based on the result of the area analysis has been described, but other adaptations are possible.

For example, in the conventional example, the coefficient string is made one-dimensional by only the zigzag scan shown in FIG. 12, but it is also possible to set the scan order for each separated area. Especially when the regions are separated according to the direction of gradation change, the power concentration coefficient is different,
It is desirable from the viewpoint of coding efficiency to provide a path for preferentially scanning the power concentration coefficient.

The code table used for variable length coding can also be set for each separated area.

On the decoding side, both the scan order and the variable length code table can be switched based on the area information in the code data.

[0063]

As described above, in the present invention, the area analysis is performed on the image signal to be encoded, and the quantization matrix corresponding to the result of the area analysis is used for the encoding. As a result, even when characters are present in a part of the photographic image, the characters are encoded using the quantization characteristics suitable for the characteristics of the characters. Can be improved.

[Brief description of drawings]

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

FIG. 2 is a configuration diagram of a region analysis unit.

FIG. 3 is a configuration diagram of a waveform analysis unit.

FIG. 4 is an explanatory diagram of waveform information analysis.

FIG. 5 is a configuration diagram of a gain analysis unit.

FIG. 6 is an explanatory diagram of gain information analysis.

FIG. 7 is an explanatory diagram showing a tree structure for determining a region separation.

FIG. 8 is a diagram illustrating area division.

FIG. 9 is a diagram showing a quantization matrix designed for each region.

FIG. 10 is a configuration diagram of a conventional example.

FIG. 11 is an explanatory diagram of block extraction.

FIG. 12 is a diagram illustrating zigzag scanning.

FIG. 13 is a diagram showing an example of a quantization matrix.

FIG. 14 is a diagram showing the correspondence between the pixel power distribution and the coefficient power distribution of a pixel block having a gradation change in the horizontal direction.

FIG. 15 is a diagram showing a correspondence between a pixel power distribution and a coefficient power distribution of a pixel block having a gradation change in the vertical direction.

FIG. 16 is a diagram showing a correspondence between a pixel distribution having a gradation change in an oblique direction and a coefficient power distribution.

[Explanation of symbols]

DESCRIPTION OF SYMBOLS 1 ... Input image, 2 ... Block extraction part, 3 ... Input block, 4 ... Average value separation part, 6 ... Average value separation block, 7 ...
Area analysis unit, 12 ... Area determination unit, 13 ... Area information, 14
... Waveform analysis unit, 15 ... Shape analysis unit, 16 ... Shape index, 17 ... Waveform mapping table, 18 ... Waveform information, 20 ... Gain analysis unit, 22 ... Variance calculator, 23 ... Variance value, 24 ... Histogram counter , 25 ... Histogram information, 26 ... Gain mapping table, 27 ... Gain information, 30 ... Maximum / minimum detector, 31 ... Dynamic range ratio, 100 ... Orthogonal transform unit, 101 ... Transform coefficient, 102 ...
Quantization unit, 103 ... Quantization matrix, 104 ... Quantization matrix storage unit, 105 ... Quantization coefficient, 106 ... Variable length coding unit, 107 ... Variable length code, 108 ... Multiplexing unit,
109 ... Code data

 ─────────────────────────────────────────────────── ─── Continuation of the front page (72) Inventor Shunichi Kimura 2274 Hongo, Ebina City, Kanagawa Prefecture, Fuji Xerox Co., Ltd.

Claims (6)

[Claims] 1. A block extracting means for dividing an image signal into an input block of m × n pixels (m and n are positive integers) composed of a plurality of pixels, and orthogonal for obtaining a transform coefficient by performing orthogonal transform on the input block. Transforming means, quantizing characteristic storing means for storing a quantizing characteristic, quantizing means for quantizing the transform coefficient with the characteristic stored in the quantizing characteristic storing means to obtain a quantizing coefficient, and the quantizing coefficient In a video signal coding apparatus having a coding means for variable-length coding and a multiplexing means for multiplexing the results of variable-length coding to obtain coded data, a waveform analysis means for analyzing the waveform of the input block. An image signal coding apparatus, comprising: and switching the quantization characteristic of the quantization characteristic storage means based on a waveform analysis result. 2. The characteristic of the direction of gradation change of the mean value separation block, wherein the waveform analysis means subtracts the mean value from each pixel in the input block, and the mean value separation block obtained by the mean value separation means. A first analyzing means for analyzing a quantity, a second analyzing means for analyzing a feature quantity in the amplitude direction of the average value separation block, an analysis result of the first analyzing means and an analysis result of the second analyzing means The image signal coding apparatus according to claim 1, further comprising: a determining unit that determines a characteristic of a waveform of the input block based on the above. 3. The first analysis means is mx obtained in advance.
n pixels (m and n are positive integers) or their positive integer ratio j
The degree of approximation between each of a plurality of sets of blocks having a representative shape composed of pixels divided by (j is a positive integer) and the average value separation block is obtained, and the index of the representative shape block having the highest degree of approximation or j The index set of the block of the representative shape having the highest degree of approximation for each of the divided blocks is output as the first feature amount in the tone change direction of the input block, and at least the index or the obtained j The ratio in which the index sets match each other is used as a parameter indicating the complexity of the shape of the average value separation block, and this complexity is determined as the second value in the gradation change direction.
The image signal encoding device according to claim 2, wherein the image signal encoding device outputs the image signal as the feature amount. 4. The second analysis means sets a root mean square value of each pixel value in the mean value separation block or a value obtained by averaging absolute values as a variance value of the input block, and the variance value is 1 The result of comparison with a threshold value of at least one kind is output as the first feature amount in the amplitude direction, the cumulative frequency distribution of each pixel value in the average value separation block is obtained, and the shape of this cumulative frequency distribution is preset. After correcting one or a plurality of normalized distributions corresponding to the variance value, the results are compared,
The image signal encoding device according to claim 2, wherein the index of the distribution that is the same or closest is output as the second feature amount in the amplitude direction. 5. The second analyzing means detects a maximum value and a minimum value in the average value separation block, calculates a ratio between the maximum value and the minimum value, and compares the ratio with one or more threshold values. The image signal encoding device according to claim 2, wherein the result is output as a third characteristic amount in the amplitude direction. 6. The quantization characteristic stored in the quantization characteristic storage means divides an image into a plurality of partial images according to the analysis result by the waveform analysis means, and performs orthogonal transformation on each partial image. The image signal coding apparatus according to claim 1, wherein the image signal coding apparatus is determined based on a variance or a standard deviation of transform coefficients obtained by applying the transform coefficient.