JPH08102948A - Image coding method and image coding device - Google Patents

Abstract

PURPOSE: To reduce a data quantity by coding image of a prescribed area or only representative coefficients without coding all of transformation coefficients obtained by applying orthogonal transformation to a texture image in the case of coding the texture image. CONSTITUTION: A texture image signal is given to an orthogonal transformation processing section EP1, in which the signal is transformed into an orthogonal transformation coefficient signal, given to an area division processing section EP3 via a quantization processing section EP2, in which the orthogonal transformation coefficient is separated. An orthogonal transformation coefficient signal ED3 is given to a feature quantity calculation processing section EP4, in which a feature quantity ED4 is obtained. The ED4 and a threshold level signal ED5 are given to a comparator EP5, and when the ED4 is larger than the ED5, the comparator provides an output of logical 1 as a discrimination signal ED6 and when smaller, the comparator provides an output of logical 0. The signal ED6 is given to a multiplier circuit EP6 together with the ED3 and a coefficient is held only for a part of the ED6 being logical 1 and coefficients of other region is converted into an orthogonal transformation coefficient ED7 of logical 0, the ED7 is once latched finally in a bit rate adjustment buffer EP8 and used for a coefficient signal for the whole.

Description

Detailed Description of the Invention

[0001]

[Table of Contents] The present invention will be described in the following order. Field of Industrial Application Conventional Technology Problems to be Solved by the Invention (FIGS. 13 to 17) Means for Solving the Problems (FIG. 1, FIG. 3, FIG. 5, FIG. 1)
0) Operation (FIGS. 1, 3, 5, and 10) Embodiment (FIGS. 1 to 12) (1) 3CC encoding and decoding processing procedure (FIG. 1)
And FIG. 2) (2) Texture coding processing procedure of the first embodiment (FIG. 3 to FIG.
FIG. 7) (3) Texture coding processing procedure of the second embodiment (FIG. 8 to FIG.
Figure 12) Effect of the invention

[0002]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an image encoding method and an image encoding apparatus, and more particularly to transmitting an image on a transmission medium having a limited transmission capacity and recording and reproducing the image on a tape recorder. Can be applied to the reproduction of the global luminance signal when using a high-efficiency coding method that preserves the contour of the object.

[0003]

2. Description of the Related Art Conventionally, a high-efficiency coding method for image signals is
Redundancy is reduced by utilizing the high correlation of image signals, which is indispensable when transmitting or recording image signals. As a conventional high-efficiency coding method of an image signal, a coding method such as predictive coding that handles an image in pixel units or a discrete cosine transform (DCT (Discrete Cosine Tr
There are sub-band coding such as orthogonal transform coding and wavelet transform represented by ansform).

Predictive coding is a typical method in which DPCM (Differential Pulse Code Modulation) in a frame is performed.
The difference between the original pixel and the decoded neighboring pixel is quantized and encoded. Such a predictive coding method is effective when the required compression rate is not so high as about 1/2 to 1/4, but is not suitable for higher compression rate coding beyond that. On the other hand, orthogonal transform coding and sub-band coding are used when the compression rate is as high as 1/10 or more, and at present, a coding method using DCT is generally widely used. This is because the DCT has a fast algorithm and is easy to implement in hardware.
It is also adopted as an international standard (JPEG, MPEG).

The basic principle of the image signal coding method using the DCT utilizes the characteristic that the power of the low frequency component of the image signal is extremely large, and the frequency component of the image signal obtained by the DCT is quantized. At this time, the quantization step size of the low frequency component is small and the step size of the high frequency component is large, thereby compressing the information amount as a whole. However, there is a problem that block distortion and mosquito noise occur due to the quantization, and the block distortion due to the processing in units of macroblocks is remarkable when the coding speed is low. Become. For this reason, a new high-efficiency coding method is required to perform ultra-low bit rate image coding.

[0006]

Therefore, as an image coding method for the purpose of transmission and recording at an extremely low bit rate,
Considering that the human visual characteristics are particularly sensitive to the contour line of an object, by preserving the contour line portion in the original image, it is attempted to realize a visually excellent restoration property even at a low bit rate. The method of doing is proposed. In such an image signal coding method in which the contour line portion in the original image is stored with priority, how efficiently the contour line of the object is extracted is important.

FIG. 13 shows an edge area extracting procedure for extracting a contour line portion from an original image. That is, the input image signal AD0 of the original image is input to the edge intensity calculation processing units AP1 and AP2 that perform edge extraction using an edge detection operator such as a Sobel filter, and as a result, edge intensity signals AD1 and AD2 are obtained. This edge strength calculation processing unit A
In P1 and AP2, a Sobel filter having a tap coefficient of 3 × 3 shown in FIG. 14 is used, and an edge strength signal AD1 indicating edge strength in the horizontal direction and an edge strength signal AD2 indicating edge strength in the vertical direction are obtained. .

The edge intensity signals AD1 and AD2 are squared in multipliers AP4 and AP5, respectively, in order to obtain the sum of the absolute values of the edge intensities of the target pixels, and the edge intensity signal power AD4 in the horizontal direction and the edge intensity signal in the vertical direction are obtained. It becomes electric power AD5. Next, the edge intensity signal power AD in the horizontal direction
4 and the edge strength signal power AD5 in the vertical direction are added by the adder A
A signal AD7 indicative of the edge intensity of the pixel of interest is obtained by addition by P7.

Here, in the case where the difference between the characteristics of the edge area such as the outline of the object or the boundary line between the objects and the edge area in the texture is taken into consideration, a feature amount for separating the edge area and the texture area is obtained. It is effective to use a local gradation value change characteristic and an index value indicating a global change characteristic.

Therefore, first, as the feature amount representing the local change characteristic of the gradation value, the edge intensity of the target pixel is an important feature amount used when determining whether or not it is an edge region.
In addition, as a difference between the contours of the objects or the boundaries between the objects and the characteristics of the edge in the texture area, the change in the spatial frequency around the pixel of interest is increased, and the high pass filter (HP
Output values such as F) are used. The high pass filter used for this edge area extraction is 3 × as described above with reference to FIG.
There are 3 Sobel filters, but in addition to this, 5 × 5,
A filter having a different tap number or tap coefficient, such as 7 × 7, can also be applied.

Further, as the characteristic amount showing the global characteristic, an average value of the output values of the high-pass filter, which is the characteristic amount showing the local change characteristic, in a certain region is used. Therefore, a mask region for obtaining a local feature amount required when obtaining a feature amount showing such a global change characteristic is set around the attention region. As the type of this mask area,
There is a window area as shown in FIG. The mask regions MSK1 and MSK2 have a pixel of interest PL bounded by the pixel of interest PL in the normal direction of the edge intensity of the pixel of interest PL.
The one-dimensional regions on both sides of the one side and the one-dimensional regions on one side are referred to as first and second mask regions MSK1 and MSK2, respectively. This is to obtain a change in gradation value that greatly changes with the edge region as a boundary from the relationship between the feature amounts on both sides of the edge.

The processing procedure of threshold value determination for edge area extraction using such various feature quantities is represented by processing units AP3, AP6, AP8 in FIG. First, an output signal AD3 obtained by inputting the pixel signal AD0 of the original image to the filtering processing unit AP3 is input to the mask area setting processing unit AP6. The feature amount AD6 calculated from the mask area set here is calculated,
In the threshold value determination processing unit AP8, the threshold value AD8 is obtained according to the signal AD7 indicating the edge intensity and the threshold value function F () having the feature amount AD6 as a parameter.

It should be noted that the function F () is determined by the simulation so that a threshold value that is considered to be optimal for separating the texture area and the edge area can be obtained. FIG. 9 shows an example of the threshold value function used in the threshold value determination processing unit AP8. As an example of the threshold function, FIG.
A function F ₁ () as shown in 6 is used which indicates the relationship between the edge intensity E of the pixel of interest and the average values M ₁ and M ₂ in the two mask areas. The threshold function F ₁ () is

[Equation 1] The edge strength E that is the threshold value T is obtained from the average values M ₁ and M ₂ in the mask area.

After the threshold value is determined, the signal AD7 indicating the edge intensity and the threshold value signal AD8 output from the threshold value determination processing unit AP8 are input to the comparator AP9 so that the pixel of interest is textured. Determine whether it is an area or an edge area. As the output signal AD9 from the comparator AP9, "0" is obtained when the attention area is the texture area, and "1" is obtained when the attention area is the edge area. Therefore, the image obtained by using the output signal AD9 from the comparator AP9 as the pixel value of each pixel is an image showing the edge region extracted by the edge region extraction, and this is hereinafter referred to as a mask image. Furthermore, in order to extract the contours between objects and the boundaries between objects even in a region where the luminance change is small, an edge separation method is used in which edge information is obtained not only from the luminance signal but also from color difference signals and synthesized.

The edge image thus obtained includes
Global features such as the contours and boundaries of objects in the original image are included, but little information such as small changes in tone values are included. Therefore, it is necessary to add the difference image between the original image and the edge image to the reconstructed edge image as information including a minute change in gradation value such as the pattern of the object. The difference image between the original image and the edge image is called the texture image,
By adding to the reconstructed edge image, it is possible to reproduce the subtle unevenness of the lawn or forest.

FIG. 17 shows a conventional procedure for encoding a textured image. The difference image signal BD0 between the original image and the edge image is input to the orthogonal transformation processing unit (DCT) BP1 as a texture image signal, and the orthogonal transformation coefficient signal BD1 is input.
Becomes At this time, the DCT or Haar transform is used as the orthogonal transform, and the transform is performed for each macroblock on the entire image. The orthogonal transform coefficient signal BD1 is supplied to the quantization processing unit B.
After being input to P2 and converted into a quantized representative value signal BD2, it is converted into a coded texture signal BD3 in a variable length coding processing unit (VLC) BP3.

The encoded texture signal BD3 is temporarily held in the bit rate adjusting buffer BP4, and then, the encoded texture image signal BD for the entire original image.
It is output as 4. At this time, the coded texture signal BD4 is fed back to the quantization processing unit BP2, and the quantization step is changed for bit rate adjustment. In this way, the texture information transmitted by the conventional method is DCT or H for the entire difference image between the original image and the edge image.
A method of quantizing orthogonal transformation coefficients obtained by performing orthogonal transformation such as aar transformation and then reducing the amount of information by using entropy coding using statistical features such as Huffman coding is used. .

When an encoding method for preserving the contour line of the original image is used for image signal encoding at an ultra-low bit rate as described above, important contour lines in the original image are extracted. The edge image thus obtained does not include minute changes in the gradation value in the original image. Therefore, the difference image between the original image and the edge image is used as the texture image, encoded separately from the edge image, and added to the edge image when reconstructing so that minute changes in the original image can be reproduced. .

However, in the conventional texture image encoding method, since the texture image for the entire surface of the original image is orthogonally transformed and quantized by a uniform quantization step, when the edge information and the texture information after encoding are combined. However, the ratio of textured information becomes large and it is difficult to increase the compression rate. For this reason, it is necessary to reduce the data amount of the textured information after encoding without degrading the image quality of the restored image as much as possible.

The present invention has been made in consideration of the above points, and when the original image is separated into the edge information and the texture information and is encoded, the data of the texture information after encoding is kept as much as possible without deteriorating the image quality of the restored image. An object of the present invention is to propose an image encoding method and an image encoding device that can reduce the amount.

[0021]

In order to solve such a problem, the present invention converts an original image (CD0) into edge information (CD0).
2) and the texture information (CD4) are separated and encoded, in the image encoding method, the texture image (CD4 (E
D0)) is orthogonally transformed and then entropy-encoded,
The obtained orthogonal transform coefficient (ED1) is set to a plurality of regions (ED
It is divided into 3), and only the orthogonal transform coefficient (ED7) in the region showing a specific feature is coded.

Further, in the present invention, the original image (CD0)
In the image coding method, in which is separated into edge information (CD2) and texture information (CD4) and is encoded, the orthogonal transformation coefficient obtained when the texture image (CD4 (FD0)) is orthogonally transformed and then entropy-encoded. (FD1) is divided into a plurality of areas (FD3), and each area (FD3)
Among the orthogonal transform coefficients for each, only the coefficient (FD7) showing a specific feature such as the maximum value or N rank is coded.

Further, in the present invention, the original image (CD0)
Is separated into edge information (CD2) and texture information (CD4) and is encoded. In the image encoding method, when the texture image (CD4 (HD1)) is encoded, the vicinity of the edge area (HD2) is excluded. Only the texture information (HD6) in the area is encoded, and the texture information near the edge area (HD2) is removed.

Further, according to the present invention, in the image coding method of separating the original image into the edge information and the texture information and encoding the texture information, the texture information is encoded according to the distance from the edge area when the texture image is coded. The weighting coefficient is changed.

Further, in the present invention, the original image (CD0)
In the image coding apparatus for separating and coding the edge information (CD2) and the texture information (CD4), the orthogonal transformation coefficient obtained when the texture image (CD4 (ED0)) is orthogonally transformed and then entropy-encoded (ED1) is divided into a plurality of regions (ED3), and encoding means (EP1 to EP8) for encoding only the orthogonal transform coefficient (ED7) in the region showing a specific feature is provided.

Further, in the present invention, the original image (CD0)
In the image coding device for separating and coding the edge information (CD2) and the texture information (CD4), the orthogonal transform coefficient obtained when the texture image (CD4 (FD0)) is orthogonally transformed and then entropy-encoded. (FD1) is divided into a plurality of areas (FD3), and each area (FD3)
Coding means (FP1 to FP8) for coding only the coefficient (FD7) showing a particular feature such as the maximum value or the N rank among the above orthogonal transform coefficients is provided.

Further, in the present invention, the original image (CD0)
Is separated into edge information (CD2) and texture information (CD4), and the image encoding apparatus encodes the texture image (CD4 (HD1)) by excluding the vicinity of the edge area (HP2). Encoding means (HP1 to H1) that encodes only texture information (HD6) in the area and removes texture information near the edge area (HD2).
P3) is provided.

Further, in the present invention, in the image coding apparatus for separating the original image into the edge information and the texture information and coding the texture information, the texture information is encoded according to the distance from the edge area when the texture image is coded. An encoding means for changing the weighting coefficient is provided.

[0029]

[Function] Texture image (CD4 (ED0, FD0))
Texture image (ED0, FD0) when encoding
Orthogonal transformation coefficients (ED1, FD
By encoding only a certain area or representative coefficients (ED7, FD7) instead of encoding all 1), the data amount of textured information after encoding is more than that in the case of using the conventional method. Can be reduced, and a reconstructed image that is visually excellent can be obtained even with the same data amount.

Further, the texture image (CD4 (HD
1)) is limited to the area to be encoded and stored, or the weighting coefficient for each pixel of the texture image is changed according to the characteristics of the area, so that the texture information after encoding is more than that when the conventional method is used. The amount of data can be reduced, and a visually reconstructed image can be obtained even with the same amount of data.

[0031]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS An embodiment of the present invention will be described in detail below with reference to the drawings.

(1) 3CC Encoding and Decoding Processing Procedure An embodiment of the present invention will be described with reference to FIGS. The present invention is a method of encoding texture information from which the edge information of the original image has been removed, and is used as part of an image encoding method at an ultra-low bit rate. In this embodiment, encoding of texture information obtained by removing edge information from an original image will be described. 3CC (3 Componen is a typical image encoding method that emphasizes the preservation of the contour line of an object.
t Coding). In 3CC, the original image is divided into local luminance information, edge information, and texture information according to the visual importance of the image, and encoding is performed according to the importance of each information. A 3CC encoding process procedure is shown in FIG. 1, and a decoding process procedure is shown in FIG.

In the 3CC encoding process, in FIG. 1, first, a local luminance generation and encoding processing unit CP1 for obtaining and encoding a local luminance (Local Luminance) component representing global luminance information of an image from the input image signal CD0. And the edge information signal CD2 output from the edge information detection and encoding processing unit CP2 for extracting and encoding the contour line of the image, that is, the edge information (Edge Informatoin) portion. Furthermore, from the local luminance signal CD1 and the edge information signal CD2, an edge information decoding and reconstruction processing unit CP3 for reconstructing an edge image.
The texture image signal CD4 is obtained by obtaining the difference between the reconstructed edge image signal CD3 obtained in (1) and the input image signal CD0. The texture image signal CD4 is
The output signal CD5 is coded by the texture information coding processing unit CP4 that performs entropy coding. Accordingly, the input image signal CD0 is finally converted into the encoded LL signal CD1, the encoded edge information signal CD2 and the encoded texture signal CD5.

On the other hand, in the decoding process of 3CC, in FIG. 2, the encoded local luminance signal DD1 is first input to the local luminance decoding and reconstruction processing unit DP1 to obtain a restored signal DD4. Encoded edge information signal DD2
The edge reconstructed image signal DD5 is obtained by inputting the signal DD4 in which the local luminance is restored to the edge information decoding / reconstruction processing unit DP2. Further, the encoded texture signal DD3 is also decoded by the texture information decoding processing unit DP3 and becomes the texture image signal DD6. Finally, the edge reconstructed image signal DD5 and the texture image signal DD6 are added to obtain a reconstructed image signal DD7.

Therefore, in 3CC, in the encoding process CP4 of the texture image in the encoding process procedure, as shown in FIG.
7, the orthogonal transformation process of the texture image corresponding to the above-mentioned process steps BP1 to BP4 and the entropy coding process are performed. In 3CC, the image is encoded with emphasis on the preservation of the contour line of the object, and most of the image information such as the contour line is included in the local luminance signal CD1 and the edge information signal CD2.

However, since high frequency components such as fine patterns in the image are included in the texture image signal CD4, in order to improve the image quality after reconstruction, information of the texture image is necessary. It is necessary to have a method for efficiently encoding important information in the. Like the traditional method,
When all the orthogonal transform coefficients of the texture image are encoded,
Information that is not so important is also uniformly included in the reconstructed image, and an unnecessarily large amount of data is required to obtain a certain image quality. In this embodiment, in determining the image quality of the reconstructed image, by deleting the coefficient that seems to be insignificant or the coefficient that seems to deteriorate the coding efficiency from the orthogonal transform coefficients of the texture image, the code Reduce the amount of data after conversion.

(2) Texture coding process procedure of the first embodiment (2-1) Orthogonal transform region selection coding In this embodiment, in determining the image quality of the reconstructed image, the orthogonal transform coefficient of the texture image is determined. Among these, the amount of data after encoding is reduced by deleting a coefficient that is considered to be insignificant or a coefficient that is considered to deteriorate coding efficiency. The procedure of the texture encoding process will be described with reference to FIGS. As the texture encoding procedure, first, the encoding procedure by selecting the orthogonal transformation area is shown in FIG.
And shown in FIG. This method divides the coefficient obtained by orthogonal transformation into several areas, saves the coefficient in the area that seems to be important for the image quality of the reconstructed image, and replaces the other coefficients with "0". This is to increase the number of zero runs during entropy coding and reduce the amount of data after coding.

In the texture image coding processing of FIG. 3, in addition to the texture image signal ED0 as an input signal,
A threshold value signal ED5 for discriminating the orthogonal transform coefficient region to be transmitted is used. First, the texture image signal ED0 is input to the orthogonal transform processing unit (DCT) EP1 and becomes the orthogonal transform coefficient signal ED1. The orthogonal transformation signal ED1 is input to the quantization processing unit EP2, converted into the quantized representative value signal ED2, and then divided into a plurality of regions in the region division processing unit EP3. Here, FIG. 4A shows orthogonal transform coefficients obtained by performing DCT on texture pixels in units of 8 × 8 macroblocks. At this time, the orthogonal transform coefficient is divided in the area division processing unit EP3 like the area delimited by the thick line in FIG.

The orthogonal transform coefficient signal ED3 in each region is input to the feature amount calculation processing unit EP4 for calculating the feature amount in each region, and the feature amount ED4 is obtained. The feature amount ED4 and the threshold signal ED5 are input to the comparator EP5. When the feature amount ED4 is larger than the threshold signal ED5, "1" is output as the determination signal ED6, and when it is smaller, "0" is output.
For the signal shown in FIG. 4A, the threshold value ED5 is set to "10" by using the average value of the coefficients in each region as the feature amount.
When processed as, the judgment signal E in the comparator EP5
The area where D6 is "1" is the area indicated by the diagonal lines.

Next, the decision signal ED6 is input to the multiplication circuit (MUX) EP6 together with the intra-region orthogonal transform coefficient signal ED3, and the decision signal ED6 holds only the coefficient of the portion showing "1", and the coefficients of the other areas. Are all orthogonal transform coefficient signals ED7 having "0". The orthogonal transform coefficient signal ED7 for the signal in FIG. 4 (A) is shown in FIG. 4 (B). Finally, the orthogonal transform coefficient signal ED7 is processed by the variable length coding processing unit (VLC).
The coded coefficient signal ED8 is input to EP7.

At this time, the orthogonal transformation coefficient signal ED7 contains many coefficients "0" and a long zero run can be taken at the time of encoding, so that the compression rate can be increased, while
Since the main coefficient part necessary for the inverse orthogonal transform is retained, the deterioration of the image quality can be minimized. Further, the orthogonal transform coefficient signal ED7 is finally held in the bit rate adjusting buffer EP8 and then becomes the coding coefficient signal ED9 for the entire macroblock. For the bit rate adjustment, the coded coefficient signal ED9 is fed back to the quantization processing unit EP2 to change the quantization step.

(2-2) Orthogonal Transform Coefficient Selection Encoding Next, as a texture encoding process procedure, the encoding process procedure by orthogonal transform coefficient selection is shown in FIGS. 5 and 6. This method divides the coefficient obtained by orthogonal transformation into several areas, stores only the coefficient satisfying a specific condition in the area, and replaces the other coefficients with "0", thereby performing entropy coding. The number of zero runs is increased and the amount of data after encoding is reduced. The specific conditions used there are various conditions such as maximum value, N rank, minimum value, and average value or more in the area.

In the texture image coding process of FIG. 5, in addition to the texture image signal FD0 as an input signal,
A threshold signal FD5 for determining the number of orthogonal transform coefficients to be transmitted is used. First, the texture image signal FD0 is input to the orthogonal transform processing unit (DCT) FP1 and becomes the orthogonal transform coefficient signal FD1. The orthogonal transformation signal FD1 is input to the quantization processing unit FP2, converted into the quantized representative value signal FD2, and then divided into a plurality of regions by the region division processing unit FP3. Here, FIG. 6A shows orthogonal transform coefficients obtained by performing DCT on texture pixels in units of 8 × 8 macroblocks. At this time, the orthogonal transformation coefficient is divided in the area division processing unit FP3 as surrounded by the thick line in FIG. 6 (A).

Here, as the condition of the coefficient to be stored, the coefficient having the maximum value and the second value in each area is stored. Therefore, the orthogonal transform coefficient signal FD3 in each region is
In order to rank the coefficients in each region, the orthogonal transformation coefficient sorting circuit FP4 is input and the sorted orthogonal transformation coefficient signal FD4 is obtained. The sorted orthogonal transform coefficient signal FD4 and the threshold value signal FD5 are input to the comparator FP5, and when the rank of the sorted orthogonal transform coefficient signal FD4 is higher than the threshold value signal FD5, “1” is output as the determination signal FD6,
If it is smaller, "0" is output.

When the threshold signal FD5 is processed as "2" for the signal shown in FIG. 6A, the comparator F
The coefficient for which the determination signal FD6 is "1" in P5 is the portion circled by the dotted line. Next, the determination signal FD6 is supplied to the multiplication circuit (MUX) together with the in-region orthogonal transform coefficient signal FD3.
The coefficient of only the portion input to the FP6, where the determination signal FD6 indicates "1", is held, and the coefficients of other areas are all "0".
The orthogonal transform coefficient signal FD7 having An orthogonal transform coefficient signal FD7 for the signal in FIG. 6 (A) is shown in FIG. 6 (B). Finally, the orthogonal transformation coefficient signal FD7 is input to the variable length coding processing unit (VLC) FP7 and the coding coefficient signal FD7 is input.
It becomes 8.

At this time, the orthogonal transform coefficient signal FD7 contains many coefficients "0" and a long zero run is taken during the encoding, so that the compression rate can be increased while the inverse orthogonal transform is performed. Since the main coefficient part necessary for the above is retained, deterioration of image quality can be minimized.
Further, the orthogonal transform coefficient signal FD7 is finally held in the bit rate adjustment buffer FP8, and thereafter,
It becomes the coding coefficient signal FD9 for the entire macroblock. At this time, the coding coefficient signal FD9 is fed back to the quantization processing unit FP2 for bit rate adjustment,
Change the quantization step.

(2-3) Effects of the First Embodiment As described above, according to the above-described encoding procedure by the orthogonal transform region selection and the orthogonal transform coefficient selection, the image quality after the reconstruction is not significantly deteriorated after the encoding. It is possible to suppress the amount of texture information, and to further improve the image quality after reconstruction by allocating the amount of data that has been able to increase or reduce the compression rate to the edge information. Also, instead of dividing the orthogonal transform coefficient in the macroblock into multiple regions,
It is also possible to select and store only a specific coefficient from all the coefficients in the macroblock as a single area.

Further, for encoding by selecting an orthogonal transformation area,
By adding the feedback circuit shown in FIG. 7, it is possible to obtain an encoding algorithm capable of adjusting the amount of generated information after texture encoding. In FIG. 7, when it is determined that the adjustment by the quantization processing unit GP2 is insufficient for adjusting the generated information amount, the output texture image signal GD6 is input to the determination signal determination circuit GP6, so that the generated information amount is large. In this case, the threshold is lowered, and when the amount of generated information is small, the threshold is raised. The feed back circuit shown in FIG. 7 is a circuit for the orthogonal transform area selection coding circuit described above with reference to FIG. 3, and the threshold value of the orthogonal transform coefficient selection coding circuit described above with reference to FIG. The value can be changed.

According to the above configuration, when the texture image is encoded, the orthogonal transform coefficient obtained by performing the orthogonal transform and then the entropy coding is divided into a plurality of regions, and the orthogonal transform is performed in each of the regions. Of the transform coefficients, by encoding only the coefficient showing a specific feature such as maximum value or N rank, or by encoding only the coefficient in the region showing the specific feature,
It is possible to suppress the amount of texture information after encoding without significantly deteriorating the image quality after reconstruction, and further improve the image quality after reconstruction by assigning the amount of data that has increased the compression rate or reduced it to edge information. You can

(3) Texture coding process procedure of the second embodiment Here, when the entire texture image is coded as in the conventional case, information that is not so important is uniformly included in the reconstructed image. There is a possibility that an excessive amount of data will be required to obtain a certain image quality. Therefore, in determining the image quality of the reconstructed image, by deleting the texture information in the area that seems to be insignificant in the texture image or the area that seems to deteriorate the coding efficiency, the amount of data after coding can be reduced. Can be reduced. The region near the region extracted as the edge region is raised as a region that deteriorates the coding efficiency.

For example, when there is a signal having a gradation value as shown in FIG. 8A, it is assumed that pixels indicated by hatching are selected as the edge area. In 3CC, the pixel values of the pixels extracted as edges are stored, and the other pixel values are restored from the pixel values obtained by sampling after smoothing the pixel values of the original pixels by LPF as local luminance signal components. To be done. In the edge image reconstruction processing, the pixel value of the edge pixel and the pixel value restored from the local luminance signal are
It is indicated by a solid line in FIG. However, the pixel values of all original pixels are shown by dotted lines.

At this time, if a diffusion (Diffusion) process is used as a method for reconstructing an edge image, the pixels near the edge are affected by the pixel values of the surrounding pixels and are easily displaced from the values of the original pixels. There's a problem. As a result, the error between the pixel value after reconstruction and the pixel value of the original pixel is as shown in FIG. 8C, and it can be seen that the error becomes large around the edge. Further, since 3CC quantizes the pixel value of the edge pixel at the stage of the edge encoding processing, an error occurs in the pixel value of the edge pixel itself, and the error around the edge pixel further increases. Since the edge reconstructed image has many errors with respect to the original image in the vicinity of the edge area, the coefficient after orthogonal transformation becomes large, which leads to an increase in the amount of texture information after encoding.

Therefore, in this embodiment, the entire texture image is not encoded as in the conventional case, but the encoding is performed by removing the vicinity of the edge area when encoding as shown in FIG. In the figure, the solid line indicates the edge area, and the area within the dotted line indicates the texture area within a certain distance from the edge area. In this texture image coding method, the texture information in the area within the dotted line is deleted, and only the texture information in the other areas is coded.

FIG. 1 shows the texture encoding procedure.
It demonstrates using 0-FIG. First, FIG. 10 shows an encoding processing procedure by limiting the first texture image area. In this texture image encoding process, in addition to the texture image signal HD1 as an input signal, an edge image signal HD
2 and the distance signal H that determines the range to remove the texture
D3 is used. First, the texture image signal HD1 is input to the two-point distance calculation circuit HP1 together with the edge image signal HD2, and the minimum distance between each pixel of the texture image and the edge pixel is obtained. The minimum distance signal HD4 between the pixel of interest and the edge pixel is input to the comparator HP2 and the distance signal HD3 is input.
Compared to. At this time, the minimum distance signal HD4 is the distance signal H.
When it is larger than D3, "1" is output as the determination signal HD5, and when it is smaller, "0" is output.

Next, the judgment signal HD5 is inputted to the buffer HP3 together with the texture image signal HD1 and the judgment signal H5 is inputted.
When D5 is "1", the pixel value of the texture image is output as the output image signal HD6, and the determination signal HD5 is "0".
In this case, the output image signal HD6 becomes "0", and as a result, the texture image information is deleted. Therefore, the texture area to be encoded as the texture information is an area in which the distance from the closest edge pixel is larger than the specified distance. The output image signal HD6 is input to the texture image encoding processing circuit shown in FIG. 17, and 3 shown in FIG.
It becomes the textured image signal CD5 after encoding in the CC processing section. By using this circuit, the region having a relatively large value in the texture image can be deleted, so that the coefficient after orthogonal transformation can be reduced and the efficiency of entropy coding can be improved.

The second texture image coding process does not limit the texture region to be coded as described above, but each pixel of the texture image is preliminarily determined according to the distance from the edge before the orthogonal transformation. Are weighted so that the coefficient after orthogonal transformation does not become large. FIG. 11 shows the weighting coefficient for each pixel of the texture image. In this figure, the horizontal axis represents the distance from the edge pixel, and the vertical axis represents the value of the weighting coefficient for each pixel.
The weighting coefficient takes a value of "0.0" to "1.0", and the product of each pixel by the weighting coefficient becomes the pixel value of the texture image to be encoded.

The above-mentioned encoding process of the first texture image is shown in FIG. 11 (A) when the weighting coefficient is shown, whereas in the second encoding process, as shown in FIG. 11 (B),
Increase the weighting factor in proportion to the distance from the edge pixel. As a result, as compared with the first case, the reconstructed image obtained by the second process is characterized in that the boundary between the region to which the texture image is added and the region to which the texture image is not added is not clear. Further, as the function for determining the weighting coefficient used in this second processing, not only a linear function as shown in FIG. 11B, but also a quadratic function or a higher-order function may be applied.

By using such an encoding procedure, it is possible to suppress the amount of texture information after encoding without significantly deteriorating the image quality after reconstruction, and to increase the compression rate or reduce the amount of data that can be reduced. By assigning it to the edge information, the image quality after reconstruction can be further improved.

Furthermore, by adding the feedback back circuit shown in FIG. 12 to the above-described encoding processing by limiting the texture image area, an encoding algorithm capable of adjusting the amount of generated information after texture encoding can be realized. FIG.
In order to adjust the amount of generated information, the output texture image signal I
By inputting D6 to the determination signal determination circuit, the range to be deleted is widened when the generated information amount is large, and the deleted range is narrowed when the generated information amount is small. Similarly, in the above-described second encoding process in which a feedback back circuit for adjusting the generated information amount is added, the function for determining the weighting coefficient may be changed depending on the data amount of the output signal.

According to the above configuration, when the texture image is encoded, the entire texture image is not encoded, but the area for encoding and storing the texture image is limited according to the degree of importance, and the texture image is stored. By changing the weighting coefficient for the texture information according to the distance from the area, the amount of data after encoding can be reduced.

[0061]

As described above, according to the present invention, when the texture image is encoded, not all the orthogonal transform coefficients obtained by orthogonally transforming the texture image are encoded.
By encoding only a certain area or a representative coefficient, the data amount of textured information after encoding can be reduced as compared with the case of using the conventional method, and even if the same data amount, it is possible to reproduce visually excellent. An image coding method and an image coding apparatus capable of obtaining a constituent image can be realized.

Further, by restricting the area to be encoded and stored in the texture image, or changing the weighting coefficient for each pixel of the texture image according to the characteristics of the area, after the encoding is performed, compared with the case of using the conventional method. It is possible to realize the image coding method and the image coding apparatus which can reduce the data amount of the texture information and can obtain a visually excellent reconstructed image even with the same data amount.

[Brief description of drawings]

FIG. 1 is a premise of an image coding method according to the present invention, 3C.
It is a block diagram which shows the encoding processing procedure of C.

FIG. 2 is a block diagram showing a decoding processing procedure of 3CC.

FIG. 3 is a block diagram showing an orthogonal transform area selection coding circuit according to the present invention.

FIG. 4 is a schematic diagram showing 8 × 8 DCT transform coefficients and transform coefficients after region selection processing.

FIG. 5 is a block diagram showing an orthogonal transform coefficient selection coding circuit according to the present invention.

FIG. 6 is a schematic diagram showing 8 × 8 DCT transform coefficients and transform coefficients after coefficient selection processing.

FIG. 7 is a block diagram showing an orthogonal transform area selection coding circuit with a feed back circuit.

FIG. 8 is a schematic diagram for explaining a pixel value distribution of texture information.

FIG. 9 is a schematic diagram for explaining a texture image area.

FIG. 10 is a block diagram showing a texture image area determination circuit.

FIG. 11 is a characteristic curve diagram for explaining a pixel value distribution in texture information.

FIG. 12 is a block diagram showing a texture image region determination circuit with a feed back circuit.

FIG. 13 is a block diagram showing a conventional procedure for extracting an edge area in an image signal.

FIG. 14 is a schematic diagram showing tap coefficients of a Sobel filter used to extract edge regions in the horizontal direction and the vertical direction.

FIG. 15: Used to obtain global characteristics of gradation values 1
FIG. 6 is a schematic diagram used to explain a three-dimensional mask region.

FIG. 16 is a characteristic curve diagram for explaining a threshold value determination function for adaptive threshold processing.

FIG. 17 is a block diagram showing a conventional texture encoding processing procedure.

[Explanation of symbols]

CP1 ... Local luminance generation and coding processing unit, CP2 ...
Edge information detection and coding processing unit, CP3 ... Edge information decoding and reconstruction processing unit, CP4 ... Texture information coding processing unit, DP1 ... Local luminance decoding and reconstruction processing unit, DP2 ... Edge information Decoding and reconstruction processing unit,
DP3 ... Texture information decoding processing unit, EP1, FD
1, GP1 ... Orthogonal transform processing unit (DCT), EP2, F
P2, GP2 ... Quantization processing unit, EP3, FP3, GP
3 ... Region division processing unit, EP4, GP4 ... Feature amount calculation processing unit, EP5, FP5, GP5, HP2, IP2 ...
Comparator, EP6, FP6, GP6 ... Multiplication circuit (MU
X), EP7, FP7, GP7 ... Variable length coding processing unit, EP8, FP8, GP8, HP3, IP3 ... Buffer, FP4 ... Orthogonal transformation sort circuit, HP1, IP1
...... Circuit for calculating the distance between two points.

─────────────────────────────────────────────────── ─── Continuation of the front page (51) Int.Cl. ⁶ Identification code Internal reference number FI Technical display location H03M 7/40 9382-5K H04N 1/41 B

Claims (14)

[Claims] 1. In an image encoding method for separating an original image into edge information and texture information and encoding the same, when the texture image is orthogonally transformed and then entropy-encoded, the obtained orthogonal transformation coefficients are divided into a plurality of regions. An image encoding method, characterized by dividing and encoding only the orthogonal transform coefficient in the region showing a specific feature. 2. In an image coding method for separating an original image into edge information and texture information and coding the obtained texture information, when the texture image is orthogonally transformed and then entropy-encoded, the obtained orthogonal transformation coefficients are divided into a plurality of regions. An image coding method, characterized by dividing and coding only coefficients showing specific characteristics such as maximum value and N rank among the orthogonal transform coefficients in each area. 3. The image code according to claim 1, wherein the amount of generated information after encoding is adjusted by adjusting the number of the orthogonal transform coefficients to be encoded. Method. 4. An image encoding method for separating an original image into edge information and texture information and encoding the texture information, wherein only the texture information in the area excluding the vicinity of the edge area is encoded when the texture image is encoded. An image coding method characterized by coding and removing the texture information in the vicinity of the edge area. 5. An image coding method for separating an original image into edge information and texture information and encoding the texture image, wherein when the texture image is encoded, a weighting coefficient for the texture information is set according to a distance from the edge area. An image encoding method characterized by varying. 6. The image code according to claim 4, wherein the amount of generated information after encoding is adjusted by changing the size of the area where the texture information is removed in the vicinity of the edge area. Method. 7. The image coding method according to claim 5, wherein the generated information amount after coding is adjusted by changing a weighting coefficient of the texture information near the edge area. 8. An image coding apparatus for separating an original image into edge information and texture information and coding the same, when the texture image is orthogonally transformed and then entropy-coded, the obtained orthogonal transformation coefficients are divided into a plurality of regions. An image coding apparatus, comprising: a coding unit that divides and codes only the orthogonal transform coefficient in the region showing a specific feature. 9. An image encoding apparatus for separating an original image into edge information and texture information and encoding the information, wherein the orthogonal transformation coefficient obtained when the texture image is orthogonally transformed and then entropy-encoded is divided into a plurality of regions. An image coding apparatus, characterized by comprising coding means for dividing and coding only a coefficient showing a particular feature such as a maximum value or N rank among the orthogonal transform coefficients in each region. 10. The encoding means adjusts the amount of generated information after encoding by adjusting the number of the orthogonal transform coefficients to be encoded.
Alternatively, the image encoding device according to claim 9. 11. An image encoding apparatus for separating an original image into edge information and texture information and encoding the texture image, when encoding the texture image, only the texture information in the area excluding the vicinity of the edge area is encoded. An image coding apparatus, comprising coding means for coding and removing the texture information in the vicinity of the edge area. 12. An image coding apparatus for separating an original image into edge information and texture information and encoding the weighted coefficient for the texture information according to the distance from the edge area when the texture image is encoded. An image encoding device comprising an encoding means for changing the image encoding device. 13. The encoding means adjusts the amount of generated information after encoding by changing the size of the area for removing the texture information near the edge area. 11. The image encoding device according to item 11. 14. The coding means according to claim 12, wherein the coding means adjusts the generated information amount after coding by changing a weighting coefficient of the texture information in the vicinity of the edge area. Image coding device.