CN102413330B - Texture-adaptive video coding/decoding system - Google Patents

Abstract

The invention discloses a texture adaptive video encoding and decoding system. The texture adaptive video encoding system includes a video encoder and a texture analyzer at the encoding end; the texture adaptive video decoding system includes a video decoder and a texture analyzer at the decoding end; the present invention The texture adaptive video codec system incorporates the texture feature information of the video image into the video codec system to improve the compression efficiency and subjective quality of video coding.

Description

A Texture Adaptive Video Codec System

This application is a divisional application of the patent application No. 200710069093.3, the title of the invention is "Texture Adaptive Video Codec System", and the filing date is June 12, 2007.

technical field

The present invention relates to the field of signal processing and communication, in particular to a texture adaptive video encoding system, a texture adaptive video decoding system and a texture adaptive video encoding and decoding system.

Background technique

Current video codec standards, such as H.261, H.263, H.26L developed by ITU and MPEG-1, MPEG-2, MPEG-4 developed by ISO's MPEG organization, and H.264/MPEG- Both AVC (H.264 for short) and the second part of China's independent intellectual property video coding standard AVS are based on the traditional hybrid video codec framework.

An important purpose of video coding is to compress the video signal to reduce the data volume of the video signal, thereby saving the storage space and transmission bandwidth of the video signal. On the one hand, the original video signal has a huge amount of data, which is the necessity of video coding and compression; on the other hand, there is a large amount of redundant information in the original video signal, which is the possibility of video coding and compression. These redundant information can be divided into spatial redundant information, temporal redundant information, data redundant information and visual redundant information. Among them, the first three kinds of redundant information only consider the redundant information in the statistical concept between pixels, which are collectively called statistical redundant information; visual redundant information focuses more on the characteristics of the human visual system. In order to reduce the amount of video signal data in video coding, it is necessary to reduce various redundant information in the video signal. The traditional hybrid video coding framework is a video coding framework that comprehensively considers predictive coding, transform coding, and entropy coding, and focuses on reducing the statistical redundancy information of video signals. The traditional hybrid video coding framework has the following main features:

(1) Use predictive coding to reduce temporal redundant information and spatial redundant information;

(2) Use transform coding to further reduce spatial redundancy information;

(3) Use entropy coding to reduce data redundancy information;

Predictive coding includes intra-frame predictive coding and inter-frame predictive coding. A video frame compressed with intra-frame predictive coding technology is called an intra-coded frame (I frame). The encoding process of an intra-coded frame is as follows: first, the coded frame is divided into coding blocks (a form of coding unit); intra-frame prediction is performed on the coding block to obtain the residual data of intra-frame prediction; Then, the transform coefficients are quantized in the transform domain; then the two-dimensional signal is converted into a one-dimensional signal by scanning; finally, entropy coding is performed. Video frames compressed with interframe predictive coding technology are called interframe coded frames (P frames, B frames). The encoding process of an inter-frame coded frame is as follows: first, the coded frame is divided into coded blocks; the motion vector and the reference block (a form of reference unit) are obtained by using motion estimation technology for the coded block; and then the motion compensation technology is used to obtain inter-frame prediction The final residual data; then perform two-dimensional transform coding on the residual data; then quantize the transform coefficients in the transform domain; then convert the two-dimensional signal into a one-dimensional signal through scanning; finally perform entropy coding. The residual data, that is, the residual signal, has reduced spatial redundant information and temporal redundant information compared to the original video signal. If spatial redundant information and temporal redundant information are expressed by mathematical correlation, the spatial correlation and temporal correlation of the residual signal are smaller than the original video information. Then two-dimensional transform coding is performed on the residual signal to further reduce the spatial correlation, and finally the transform coefficients are quantized and entropy coded to reduce data redundancy information. It can be seen that in order to continue to improve the compression efficiency of video coding, more accurate predictive coding technology is needed to further reduce the spatial correlation and temporal correlation of the residual signal after prediction; at the same time, more effective transform coding technology is needed to further reduce the spatial correlation; At the same time, after predictive coding and transform coding, the corresponding scanning technology, quantization technology and entropy coding technology are designed.

Although the above-mentioned video codec standard based on the traditional hybrid video codec framework has achieved great success, there is a bottleneck in the traditional hybrid video codec framework itself in order to further improve the compression efficiency of video coding. The research results show that the video signal is not a stationary source, which means that the characteristics of each coding unit are different. However, the design of functional modules in the traditional hybrid video codec framework is based on the assumption of a stationary video signal. For example, when the predictive coding module, transform coding module, quantization module, scanning module, etc. The working mode adopted is fixed:

(1) When the prediction compensation is accurate to sub-pixel in inter-frame predictive coding, it is necessary to use interpolation technology to construct the sub-pixel points in the reference image. The current video standards based on the traditional hybrid video codec framework all use horizontally and vertically separable one-dimensional interpolation filters to construct sub-pixels. The number of taps and coefficients of the interpolation filter are fixed, so the interpolation filter used has nothing to do with the content of the interpolated image.

(2) The transform coding module widely adopts discrete cosine transform (DCT) technology and its approximate transform technology integer cosine transform (ICT) technology. Transform coding intends to reduce the spatial correlation, and concentrate the energy of the coding unit to a few transform coefficients. In DCT and ICT, it is concentrated on low-frequency energy. The transformation matrices are all fixed, so the transformation applied is independent of the content of the image being transformed.

(3) The quantization module is a lossy irreversible coding module for transform coefficients. The quantization technology used in the current video standard is to perform scalar quantization with the same step length on each transform coefficient or to perform rough quantization on high-frequency coefficients through quantization matrix weighting (using the insensitivity of human eyes to high-frequency signals). It can be seen that the quantization process has nothing to do with the quantized image content.

(4) The scanning module converts two-dimensional signals into one-dimensional signals. Specifically, it converts quantized two-dimensional transformation coefficients into run and level signals, so as to facilitate entropy coding of run and level signals. The current video coding standard adopts the zigzag scanning method for frame coding and the vertical priority alternative scanning method for field coding, and the scanning order of these two scanning methods is fixed, and the transformation coefficients of coding blocks are basically in the order from upper left to lower right . It can be seen that the scanning order has nothing to do with the scanned image content.

In recent years, in order to further improve the efficiency of video coding, some new coding technologies have emerged. The commonality of these technologies is "adaptive", which can select different coding methods for each frame or each coding block (referring to certain functional modules. Select the appropriate working mode). Some of the adaptive methods of these technologies are based on the rate-distortion optimization (RDO) technology, that is, through the high-complexity approach of RDO, an optimal method in the sense of RD is selected from several candidate methods; some are based on statistical The method adopts the idea of "two passes". After the first pass, the data statistics of the first pass are used to obtain the corresponding working mode, and then the second pass of encoding is performed with the corresponding working mode.

In addition, there is an adaptive transformation technique, which is based on the method of neural network. An initial transformation mode is initially set, and as the encoding progresses, a new transformation mode is gradually trained through the neural network, and the next encoding block is transformed and encoded.

The "adaptive" point of view of these methods is actually the idea of exploiting the different local characteristics of coding units, but their local characteristics are vague and general.

On the basis of the previous analysis and research, in order to break through the bottleneck of the traditional hybrid video coding and decoding framework, the present invention proposes a texture adaptive video coding and decoding system. The texture adaptive video coding and decoding system includes a texture adaptive video coding system and a texture Adapt to the video decoding system. The texture adaptive video codec system incorporates the texture feature (a kind of image local feature) information of the video image into the video codec system to improve the compression efficiency and subjective quality of video coding.

Contents of the invention

The purpose of the present invention is to propose a texture adaptive video encoding and decoding system aiming at the bottleneck of the traditional hybrid video encoding and decoding framework. The texture adaptive video codec system includes a texture adaptive video coding system and a texture adaptive video decoding system.

The purpose of the present invention is achieved through the following technical solutions: a texture adaptive video coding system, which includes a video encoder and an encoding end texture analyzer; the video encoder includes at least one encoding function module to complete encoding compression; encoding The end texture analyzer is used for texture analysis to extract the texture feature information of the coding unit; the video encoder includes a texture adaptive scanning module, and the texture adaptive scanning module selects a appropriate scan order.

Further: the texture feature information includes texture direction information, or the texture feature information includes texture direction information and texture intensity information.

Further: the input signal of the texture analyzer at the coding end includes one or more of the following: original image data, reference image data of the coding unit, and output data of the coding function module.

Further: the video encoder also includes a texture adaptive interpolation module, the texture adaptive interpolation module selects an appropriate interpolation method to construct sub-pixel points according to the texture feature information extracted by the texture analyzer at the encoding end, wherein, The texture feature information is texture direction information.

Further: the texture adaptive interpolation module and the texture adaptive scanning module use the same or different texture feature information of the coding unit for control.

A texture adaptive video decoding system, which includes a video decoder and a texture analyzer at a decoding end; the video decoder includes at least one decoding function module to complete decoding and reconstruction; the texture analyzer at the decoding end is used for texture analysis to extract decoding units The texture feature information; the video decoder includes a texture adaptive scanning module, and the texture adaptive scanning module selects an appropriate inverse scanning order according to the texture feature information extracted by the texture analyzer at the decoding end.

Further: the texture feature information includes texture direction information, or the texture feature information includes texture direction information and texture intensity information.

Further: the input signal of the texture analyzer at the decoding end includes one or more of the following: reference image data, and output data of a decoding function module.

Further: the video decoder also includes a texture adaptive interpolation module, and the texture adaptive interpolation module selects an appropriate interpolation method according to the texture feature information extracted by the texture analyzer at the decoding end to construct sub-pixel points, wherein, The texture feature information is texture direction information.

Further: the texture adaptive interpolation module and the texture adaptive scanning module use the same or different texture feature information of the decoding unit for control.

The beneficial effect of the present invention is that the texture adaptive video codec system of the present invention incorporates the texture features of video images into the video codec system, so as to improve the compression efficiency and subjective quality of video coding.

Description of drawings

Fig. 1 is a schematic diagram of a texture adaptive video coding system;

Fig. 2 is a schematic diagram of a texture adaptive video decoding system;

Fig. 3 is a schematic diagram of a texture adaptive video codec system;

Fig. 4 is a schematic diagram of the original data of an n × m coding block;

FIG. 5 is a schematic diagram of data of n×m reference image blocks;

Fig. 6 is a schematic diagram of an intra prediction mode as a signal input to a texture analyzer at the encoding end;

Figure 7 is a schematic diagram of the Sobel operator;

Figure 8 is a schematic diagram of a texture adaptive interpolation module;

Fig. 9 is a schematic diagram of integer pixels and sub-pixels;

Fig. 10 is a schematic diagram of a texture adaptive scanning module;

FIG. 11 is a schematic diagram of a vertical priority scanning sequence;

FIG. 12 is a schematic diagram of a horizontal priority scanning sequence;

Fig. 13 is a schematic diagram of implementation example 1: texture adaptive video coding system;

Fig. 14 is a schematic diagram of implementation example 2: texture adaptive video decoding system;

Fig. 15 is a schematic diagram of implementation example 4: texture adaptive video coding system;

Fig. 16 is a schematic diagram of implementation example 5: texture adaptive video decoding system;

Fig. 17 is a schematic diagram of implementation example 7: texture adaptive video coding system;

Fig. 18 is a schematic diagram of implementation example 8: texture adaptive video decoding system;

Fig. 19 is a schematic diagram of implementation example 10: texture adaptive video coding system;

Fig. 20 is a schematic diagram of implementation example 11: texture adaptive video decoding system.

Detailed ways

The present invention relates to a texture adaptive video coding system (shown in Figure 1), a texture adaptive video decoding system (shown in Figure 2) and a texture adaptive video coding system (shown in Figure 3), note The "transport channel" shown in Figure 3 is not included in the texture adaptive video codec system.

The texture adaptive video coding and decoding system involves a wide range, and the terms involved in the present invention will be described with examples below.

A. Examples of coding units

A coding unit is a texture-adaptive unit, which is a collection of video pixels. There are many forms of coding units. In the early differential pulse modulation coding system, the coding unit is a single pixel; in many current video coding standards, the coding unit is a rectangular pixel block, including a square; and the latest literature mentioned The coding unit is in different forms such as triangle and trapezoid; the coding unit can also be in the form of a slice, a frame, a field, etc.; in addition, the coding unit can also be composed of non-adjacent pixels.

A coding block is an example of a coding unit, which is a rectangular block composed of pixels. The size of the rectangular block is n×m, which means that the coding block has a height of n pixels and a width of m pixels. For example, a 16×16 coding block, a 16×8 coding block, an 8×16 coding block, an 8×8 coding block, an 8×4 coding block, a 4×8 coding block, and a 4×4 coding block. Hereinafter, a specific implementation example will be given by taking a coding block as an example. Unless otherwise specified, a coding block will be used instead of a coding unit. However, the methods listed in the implementation examples can also be used for other types of coding units.

B. Examples of decoding units

The decoding unit and the encoding unit are different expressions of the same thing in different positions of the system. A coding unit is a concept in a texture-adaptive video coding system, and correspondingly, it is called a decoding unit in a texture-adaptive video decoding system. Therefore, the example and description of the encoding unit mentioned in A is also suitable for the example and description of the decoding unit.

C. Examples of video encoding functional modules

The encoding function module in the video encoder includes one or more of the following modules: prediction module, interpolation module, transformation module, inverse transformation module, quantization module, inverse quantization module, scanning module, inverse scanning module, deblocking filtering module, entropy Encoding modules, etc. These coding functional modules can be subdivided into multiple or combined into one coding functional module. For example, the interpolation module can be divided into a half-pixel interpolation module and a quarter-pixel interpolation module; for example, the transformation module and the quantization module are combined into Transform quantization module. The video encoder may also have other functional division methods to form a new set of encoding functional modules.

The encoding function modules in the video encoder are connected in a certain way to complete the function of encoding and compression.

For an encoding function module, its working mode can be various, for example, the interpolation module adopts different numbers of filter taps and filter coefficients to be different working modes.

D. Examples of video decoding function modules

The decoding function modules in the video decoder include one or more of the following modules: prediction module, interpolation module, inverse transformation module, inverse quantization module, inverse scanning module, deblocking filtering module, entropy decoding module and the like. These decoding function modules can be subdivided into multiple or combined into one decoding function module. For example, the interpolation module can be divided into a half-pixel interpolation module and a quarter-pixel interpolation module; such as an inverse transformation module and an inverse quantization module. Combined into an inverse transform quantization module. The video decoder may also have other functional division methods to form a new set of decoding functional modules.

The decoding function modules in the video decoder are connected in a certain way to complete the function of decoding and reconstruction.

For a decoding function module, its working mode can be varied, for example, the interpolation module adopts different numbers of filter taps and filter coefficients to be different working modes.

E. Examples of texture feature information

The representation of texture feature information may include texture direction information, texture strength information, texture direction strength information, or other representations, such as texture structure.

E-1. Texture direction information

Texture direction information is subjectively expressed as the orientation of the texture in the image, which is generally represented by the texture tilt angle. The tilt angle is a continuous quantity, and it can be quantized into a discrete quantity when used. When quantizing, you can choose different precisions and divide the texture into different types of directions. During quantization, texture tilt angles whose angles belong to the same quantization area are classified as the same type of texture direction. For example, when the quantization precision is 4 grades, the texture direction information can be divided into horizontal texture, vertical texture, left diagonal texture and right diagonal texture. Of course, some coding units have no obvious texture direction, and it can also be said that the texture intensity corresponding to each texture direction is equivalent, which is called a flat area, and the flat area is a special texture direction information.

The direction of an edge in an image is an example of texture direction information.

E-2. Texture strength information

Texture intensity information is subjectively expressed as the degree of texture in the image, which can be expressed by gradient intensity, energy intensity, or other methods.

E-3. Texture direction strength information

The texture direction intensity information means that the texture directions are divided into different types according to E-1, and each type of texture direction has corresponding intensity information. The texture direction intensity information is the texture intensity information corresponding to each texture direction.

F. An example of the input signal of the texture analyzer at the encoding end

F-1. Raw image data

Raw image data refers to data that consists of or is constructed from the raw pixel values of a raw image. There are various construction methods, such as interpolation methods, filtering methods, pixel repetition methods, etc.

F-2. Reference image data

Reference image data refers to data consisting of or constructed from pixel values of a decoded reconstructed image. There are various construction methods, such as interpolation methods, filtering methods, pixel repetition methods, etc.

F-3. Output data of encoding function module

The data corresponding to the current encoding unit output by the encoding function module.

For example, the functional module is an intra-frame prediction module, and the output data is the intra-frame prediction mode of the current coding unit. Figure 6 is a schematic diagram of this example.

The data corresponding to the encoded encoding unit(s) output by the encoding function module.

For example, the functional module is an intra-frame prediction module, and the output data is the intra-frame prediction modes of the coding units above and to the left of the current coding unit. In order for the texture analyzer at the encoding end to analyze the texture features of the current coding unit, the information should be cached for a certain period of time and then input to the texture analyzer at the encoding end.

The output data of the encoding function module is not limited to the intra prediction mode output by the intra prediction module, it can be the transformation coefficient output by the transformation module; the scanning sequence output by the scanning module; the inter prediction mode output by the inter prediction module, etc.

Note that the output data of the encoding function module refers to part or all of the output data of the encoding function module, for example, the intra prediction mode here is only part of the output data of the intra prediction module.

F-4, the input signal is a variety of data

The input signal includes multiple kinds of data in the following data: original image data, reference image data and output data of the encoding function module.

For example, the original image data of the coding unit and the inter-frame matching unit of the coding unit are both sent to the texture analyzer at the coding end as input signals.

Among them, Figure 4 is an example of the original image data of the coding unit. The coding unit is a coding block, which is an n×m block P, and P _ji represents the pixel value at the (j,i)th position, which is the raw pixel value.

A matching unit is the closest reference image data to a coding unit. The pixels constituting the matching unit and the current coding unit are not in the same frame image, which is called an inter-frame matching unit; the pixels constituting the matching unit and the current coding unit are in the same frame image, which is called an intra-frame matching unit. FIG. 5 is an example of an inter-frame matching unit. The inter-frame matching unit R is a block of size n×m. R _ji represents the value of the pixel at the (j,i)th position.

G. An example of the input signal of the texture analyzer at the decoding end

G-1. Reference image data

When the input signal of the texture analyzer at the decoding end is the reference image data, it is consistent with F-2.

G-2. Decoding function module output data

The data corresponding to the current decoding unit output by the decoding function module.

For example, first, the entropy decoding module obtains the texture feature information of the current decoding unit after parsing the code stream, and outputs the texture feature information to the texture analyzer at the decoding end; second, the functional module is an intra prediction module, and the output data is the current decoding unit The intra prediction mode for .

Data corresponding to the decoded decoding unit(s) output by the decoding function module.

For example, the functional module is an intra-frame prediction module, and the output data is the intra-frame prediction modes of the decoding units above and to the left of the current decoding unit. In order for the texture analyzer at the decoding end to analyze the texture features of the current decoding unit, the information should be buffered for a certain period of time before being input to the texture analyzer at the decoding end.

The output data of the decoding function module is not limited to these examples, and it can also be the transform coefficient output by the transform module; the scan order output by the scan module; the inter prediction mode output by the inter prediction module, etc.

Note that the output data of the decoding function module refers to part or all of the output data of the decoding function module, for example, the intra prediction mode here is only part of the output data of the entropy decoding module.

G-3, the input signal is a variety of data

The input signal includes multiple kinds of data in the following data: reference image data and decoding function module output data.

For example, the inter-frame matching unit of the decoding unit and the inter-frame prediction modes of the upper and left decoding units of the decoding unit are sent as input signals to the texture analyzer at the decoding end.

H. An example of extracting coding unit texture feature information by the texture analyzer at the coding end

H-1. The input signal is the original image data

The input signal of the texture analyzer at the encoding end is as described in F-1.

Take the case where the original image data of the coding unit is the input signal as an example, where the coding unit is the coding block, and Figure 4 is an n×m coding block P, where P _ji represents the value of the pixel at the (j,i)th position, is the original data of the pixel. The texture analyzer uses the Sobel operator to extract the texture feature information of the coding block P, that is, texture direction information and texture intensity information. Fig. 7 is the calculation method of Sobel operator x-direction and y-direction gradient. According to the Sobel operator, the gradients in the x-direction and y-direction of P _ji can be obtained.

The gradient in the x direction is:

hx(P _ji )=P _j-1,i-1 +2×P _j-1,i +P _j-1,i+1 -P _j+1,i-1 -2×P _j+1,i -P _j+1,i+1

The gradient in the y direction is:

hy(P _ji )=P _j-1,i+1 +2×P _j,i+1 +P _j+1,i+1 -P _j-1,i-1 -2×P _j-1,i -P _j-1,i+1

Then the gradient direction of P _ji is

Dir(P _ji )=arctan(hy(P _ji )/hx(P _ji )), arctan is arctangent function;

The gradient strength of P _ji is

Mag(P _ji )=sqrt(hx((P _ji )^2+hy(P _ji )^2), sqrt is the root function, and ^2 refers to the square.

Quantize Dir(P _ji ) into grades of corresponding precision, and each grade corresponds to a kind of texture direction information. For example, four quantitative grades: horizontal, vertical, left diagonal and right diagonal.

In order to determine the texture direction information and texture intensity information of P, the Dir value and Mag value of the (n-2)×(m-2) pixel points from P ₁₁ to P _{n-1, m-1} are calculated in turn, according to Dir Quantify the grade, classify these points, and calculate the sum of the Mag value of the pixel points in each class, and the sum of the Mag value is the texture direction intensity information of P. The dominant texture direction intensity information is the texture intensity information of P, and its corresponding texture direction information is defined as the texture direction information of P; if there is no dominant texture intensity information in the classification, P can be considered as a flat area.

The texture analyzer can also use other operators or methods to extract texture feature information that is the same as or different from the expression in this example by using the original image data.

H-2. The input signal is the reference image data

The input signal of the texture analyzer at the encoding end is as described in F-2.

The reference image data takes an inter-frame matching unit as an example, the coding unit is a coding block, and the inter-frame matching unit is an inter-frame matching block, as shown in FIG. 5 . The method for extracting texture feature information can adopt the method exemplified in H-1.

H-3. The input signal is the output data of the encoding function module

The input signal of the texture analyzer at the encoding end is as described in F-3.

Example 1: The input signal is the intra prediction mode of the current coding unit in F-3. The intra prediction mode is directional, such as horizontal prediction mode, vertical prediction mode, left diagonal prediction mode, right diagonal prediction mode and DC prediction mode. The texture analyzer at the coding end determines the texture feature information of the coding block according to the prediction mode, here it is only the texture direction information. If the prediction mode is horizontal prediction mode, the texture direction is horizontal texture; if the prediction mode is vertical prediction mode, the texture direction is vertical texture; if the prediction mode is left diagonal prediction mode, the texture direction is left diagonal texture; if the prediction mode is Right diagonal prediction mode, the texture direction is right diagonal texture; if the prediction mode is DC prediction mode, the texture feature information is a flat area without obvious texture.

Example 2: The input signal is the inter prediction mode information of the coding unit above and to the left of the current coding unit output by the inter prediction module. The inter prediction mode refers to the block size at the time of inter prediction. For example, when the inter-frame prediction mode of the coding block above and to the left of the coding block is 16×8, it is determined that the coding block is in the horizontal texture direction; when the inter-frame prediction mode of the coding block above and to the left of the coding block is 8×8 At 16, it is determined that the coding block is in the longitudinal texture direction; in other cases, it is determined that the coding block is a flat area.

H-4. The input signal is combined data

The input signal of the texture analyzer at the encoding end is as described in F-4.

Here, the original image data is exemplified by the original data of the coding unit, and the reference image data is exemplified by the inter-frame matching unit. These two signals are used as the input signals of the texture analyzer at the encoding end, and the encoding unit is the encoding block, and the texture analyzer at the encoding end first obtains the differential signal between them, and the differential signal refers to the difference between the encoding block and the matching block between frames; The differential signal is processed by the method exemplified in H-1 to obtain the texture feature information of the coding unit.

I. An example of extracting texture feature information by the texture analyzer at the decoding end

I-1. The input signal is the reference image data

When the input signal of the texture analyzer at the decoding end is reference image data, as described in G-1, the texture feature information of the decoding unit can be extracted according to the method exemplified in H-2.

I-2. The input signal is the output data of the decoding function module

Example 1: The code stream contains the texture feature information of the decoding unit, and the input signal of the texture analyzer at the decoding end is the texture feature information of the decoding block obtained by the entropy decoding module through code stream analysis or a coded form of the texture feature information of the decoding block. This information is input to the texture analyzer at the decoding end, and the texture analyzer at the decoding end directly uses or decodes the information to obtain texture feature information of the decoding block.

Example 2: The input signal of the texture analyzer at the decoding end is the intra prediction mode of the current decoding block output by the intra prediction module. The texture analyzer at the decoding end determines the texture feature information of the decoding block according to the method exemplified in H-3.

Example 3: The input signal is the inter prediction mode information of the decoding unit above and to the left of the current decoding unit output by the inter prediction module. The texture analyzer at the decoding end determines the texture feature information of the decoding block according to the method exemplified in H-3.

J. An example of adapting the working mode of the encoding function module and the decoding function module to the texture feature

J-1, texture adaptive interpolation module

Fig. 8 is a schematic diagram of a texture adaptive interpolation module. The texture analyzer at the coding end extracts the texture feature information of the coding block, and the texture adaptive interpolation module is controlled by the texture feature information to make it choose an appropriate interpolation method, that is, the working mode, to construct sub-pixel points. The texture adaptive interpolation module in Figure 8 has N types of texture direction interpolation, and each type corresponds to a working mode. The texture feature information extracted by the texture analyzer at the encoding end includes texture direction information. After obtaining the texture direction information of the coding block, the texture adaptive interpolation module selects different working modes according to the texture direction to perform sub-pixel interpolation construction. FIG. 9 is a schematic diagram of sub-pixel points that need to be interpolated. In the figure, the uppercase letters A-P represent the position of the integer pixel; the lowercase letters a-o represent the position of the sub-pixel point corresponding to the integer pixel point A. Sub-pixels can be divided into half-pixels and quarter-pixels. Among them, b, h, and j represent half pixels, and other lowercase letters represent quarter pixels. The following table lists the interpolation working modes corresponding to different texture directions of the coding block. Of course, the design of the interpolation filter is not limited to this method, and other design methods are also possible.

The texture adaptive interpolation module located in the texture adaptive video coding system is called the texture adaptive interpolation encoding function module, and the texture adaptive interpolation module located in the texture adaptive video decoding system is called the texture adaptive interpolation decoding function module.

J-2. Example of Texture Adaptive Scanning Module

Fig. 10 is a schematic diagram of a texture adaptive scanning method. The texture analyzer at the encoding end extracts texture feature information of the encoding block, and the texture adaptive scanning module selects an appropriate scanning sequence according to the texture feature information extracted by the texture analyzer at the encoding end, and each scanning sequence corresponds to a working mode. The scanning order has a vertical priority scanning sequence; a horizontal priority scanning sequence; a direction priority scanning sequence, etc. As shown in Figure 11, it is an example of two vertical priority scanning orders for 8×8 blocks. The scanning order on the right in the figure has a higher vertical priority than the scanning order on the left; as shown in Figure 12, there are two types of 8×8 blocks. An example of a horizontal priority scanning order, the scanning order on the right in the figure has a greater horizontal priority than the scanning order on the left; the scanning order without direction priority, such as the zigzag scanning order. The texture feature information output by the texture analyzer is texture direction information and texture intensity information. The texture direction information is divided into three types: horizontal texture, vertical texture and other textures; the texture intensity information is divided into two types: strong and weak. The following table exemplifies the use of texture direction information and texture intensity information to control the working mode of the texture adaptive scanning module. For example, for strong horizontal textures, the adaptive scanning module uses the scanning sequence shown in the right figure of Figure 11 as the working mode; for Weak other textures, the adaptive scanning module uses the zigzag scanning order as the working mode, etc.

The following table exemplifies the situation where only texture direction information is used to control the working mode of the texture adaptive scanning module. For example, for horizontal textures, the adaptive scanning module uses the scanning sequence shown in the right figure of Figure 11 as the working mode; for vertical textures, the adaptive scanning module The scanning module adopts the scanning order shown in the right figure of Figure 12 as the working mode; for other textures, the adaptive scanning module adopts the zigzag scanning order as the working mode.

texture direction information Adaptive scanning module working mode horizontal texture The scan sequence shown on the right in Figure 11 longitudinal texture The scan sequence shown on the right of Figure 12 other textures zigzag scan order

The texture adaptive scanning module located in the texture adaptive video coding system is called the texture adaptive scanning coding function module, and the texture adaptive video decoding system is called the texture adaptive scanning decoding function module.

Of course, there are other examples where the encoding functional modules and decoding functional modules adapt to texture features, such as transformation modules, quantization modules, etc., and will not be listed one by one.

Implementation example 1

Fig. 13 is a schematic diagram of implementation example 1, which is a texture adaptive video coding system. The input signal of the texture analyzer at the coding end is the reference image data of the coding unit, as described in F-2, and the output information is the texture direction information of the coding unit, and the method of extracting the texture direction information is as exemplified in H-2. The output texture direction information controls the working mode of the texture adaptive interpolation module in the video encoder, so that the texture adaptive interpolation module selects an appropriate interpolation working mode according to the texture direction information by the method exemplified in J-1. Other encoding functional modules in the video encoder do not perform self-adaptation according to texture feature information.

Implementation example 2

Fig. 14 is a schematic diagram of implementation example 2, which is a texture adaptive video decoding system. The input signal of the texture analyzer at the decoding end is the reference image data of the decoding unit, as described in G-1, and the output information is the texture direction information of the decoding unit, and the extraction method of the texture direction information is as exemplified in I-1. The output texture direction information controls the working mode of the texture adaptive interpolation module in the video decoder, so that the texture adaptive interpolation module selects an appropriate interpolation working mode according to the texture direction information by the method exemplified in J-1. Other decoding functional modules in the video decoder do not perform self-adaptation according to texture feature information.

Implementation example 3

The texture adaptive video coding system implementing Example 3 includes the texture adaptive video coding system implementing Example 1 and the texture adaptive video decoding system implementing Example 2.

Implementation Example 4

Fig. 15 is a schematic diagram of implementation example 4, which is a texture adaptive video coding system. The input signal of the texture analyzer at the coding end is the reference image data of the coding unit, as described in F-2, and the output information is the texture direction information and texture intensity information of the coding unit, and the extraction method adopts the method exemplified in H-2. The texture direction information controls the texture self-adaptive interpolation module in the video encoder, so that the texture self-adaptive interpolation module selects the appropriate interpolation mode according to the texture direction information in the method exemplified in J-1; the texture direction information and the texture strength information control The texture adaptive scanning module in the video encoder enables the texture adaptive scanning module to select an appropriate scanning working mode according to the method exemplified in J-2. Other encoding functional modules in the video encoder do not perform self-adaptation according to texture feature information.

Implementation Example 5

Fig. 16 is a schematic diagram of implementation example 5, which is a texture adaptive video decoding system. The input signal of the texture analyzer at the decoding end is the reference image data of the decoding unit that needs to be decoded. As described in G-1, the output information is the texture direction information and texture intensity information of the decoding unit. The extraction method adopts the example in I-1 Methods. The texture direction information controls the texture self-adaptive interpolation module in the video decoder, so that the texture self-adaptive interpolation module selects the appropriate interpolation mode according to the texture direction information in the method exemplified in J-1; the texture direction information and the texture strength information control The texture adaptive scanning module in the video decoder enables the texture adaptive scanning module to select an appropriate inverse scanning working mode according to the method exemplified in J-2. Other decoding functional modules in the video decoder do not perform self-adaptation according to texture feature information.

Implementation Example 6

The texture adaptive video coding system implementing Example 6 includes the texture adaptive video coding system implementing Example 4 and the texture adaptive video decoding system implementing Example 5.

Implementation example 7

Fig. 17 is a schematic diagram of Embodiment 7, which is a texture adaptive video coding system. The input signal of the texture analyzer at the coding end is the original data of the coding unit to be coded, as described in F-1, the output information is the texture direction information and texture intensity information of the coding unit, and the extraction method adopts the example in H-1 method. The texture direction information controls the texture self-adaptive interpolation module in the video encoder, so that the texture self-adaptive interpolation module selects an appropriate interpolation work mode according to the texture direction information according to the method exemplified in J-1; the texture direction information and the texture strength information control The texture adaptive scanning module in the video encoder enables the texture adaptive scanning module to select an appropriate scanning working mode according to the method exemplified in J-2. Other encoding functional modules in the video encoder do not perform self-adaptation according to texture feature information.

Implementation Example 8

Fig. 18 is a schematic diagram of implementation example 8, which is a texture adaptive video decoding system. The input signal of the texture analyzer at the decoding end is the output signal of the entropy decoding module. As described in G-2, the output information is the texture direction information and texture intensity information of the decoding unit that needs to be decoded, and the extraction method of the texture direction information and texture intensity information Use the method exemplified in I-2. The texture direction information controls the texture self-adaptive interpolation module in the video decoder, so that the texture self-adaptive interpolation module selects an appropriate interpolation work mode according to the texture direction characteristic information according to the method exemplified in J-1; texture direction information and texture intensity information Control the texture adaptive scanning module in the video decoder, so that the texture adaptive scanning module selects an appropriate inverse scanning working mode according to the method exemplified in J-2. Other decoding functional modules in the video decoder do not perform self-adaptation according to texture feature information.

Implementation example 9

The texture adaptive video coding system implementing Example 9 includes the texture adaptive video coding system implementing Example 7 and the texture adaptive video decoding system implementing Example 8.

Implementation example 10

Fig. 19 is a schematic diagram of Embodiment 10, which is a texture adaptive video coding system. The input signal of the texture analyzer at the encoding end is the intra-frame prediction mode output by the intra-frame prediction module. As described in F-3, the output information is the texture direction information of the coding unit, and the extraction method of the texture direction information adopts the example in H-3 method of lifting. The texture direction information controls the texture adaptive interpolation module and the texture adaptive scanning module in the video encoder, so that the texture adaptive interpolation module selects an appropriate interpolation work mode according to the texture direction information by the method enumerated in J-1; the texture The adaptive scanning module selects an appropriate scanning working mode according to the method in J-2 according to the texture direction information. Other encoding functional modules in the video encoder do not perform self-adaptation according to texture feature information.

Implementation Example 11

Fig. 20 is a schematic diagram of Embodiment 11, which is a texture adaptive video decoding system. The input signal of the texture analyzer at the decoding end is the intra-frame prediction mode output by the intra-frame prediction module. As described in G-2, the output information is the texture direction information that needs to be decoded. Example method. The texture direction information controls the texture self-adaptive interpolation module and the texture self-adaptive scanning module in the video decoder, so that the texture self-adaptive interpolation module selects an appropriate interpolation work mode according to the method exemplified in J-1 according to the texture direction information; The adaptive scanning module selects an appropriate reverse scanning working mode according to the method exemplified in J-2 according to the texture direction information. Other decoding functional modules in the video decoder do not perform self-adaptation according to texture feature information.

Implementation example 12

The texture adaptive video coding system implementing Example 12 includes the texture adaptive video coding system implementing Example 10 and the texture adaptive video decoding system implementing Example 11.

The above-mentioned embodiments are used to illustrate the present invention, rather than to limit the present invention. Within the spirit of the present invention and the protection scope of the claims, any modification and change made to the present invention will fall into the protection scope of the present invention.

Claims (10)

1. a texture self-adaption video coded system, is characterized in that: it comprises video encoder and coding side texture analyzer; Video encoder at least comprises an encoding function module, to complete compression coding; Coding side texture analyzer is used for carrying out texture analysis, to extract the textural characteristics information of coding unit; Video encoder comprises texture self-adaption scan module, and the textural characteristics information that described texture self-adaption scan module extracts according to coding side texture analyzer, selects a kind of scanning sequency adapting.

2. texture self-adaption video coded system according to claim 1, is characterized in that: described textural characteristics packets of information is containing grain direction information, or described textural characteristics packets of information is containing grain direction information and texture strength information.

3. texture self-adaption video coded system according to claim 1, is characterized in that: the input signal of described coding side texture analyzer comprise following one or more: the reference image data of raw image data, coding unit, encoding function module output data.

4. texture self-adaption video coded system according to claim 1, it is characterized in that: video encoder also comprises texture self-adaption interpolating module, the textural characteristics information that described texture self-adaption interpolating module extracts according to coding side texture analyzer, select a kind of interpolation method adapting, build sub-pix point, wherein, described textural characteristics information is grain direction information.

5. texture self-adaption video coded system according to claim 4, is characterized in that: texture self-adaption interpolating module and texture self-adaption scan module use of the same race or coding unit textural characteristics information not of the same race to control.

6. a texture self-adaption video decode system, is characterized in that: it comprises Video Decoder and decoding end grain reason analyzer; Video Decoder at least comprises a decoding function module, to complete decoding and rebuilding; Decoding end texture analyzer is used for carrying out texture analysis, to extract the textural characteristics information of decoding unit; Video Decoder comprises texture self-adaption scan module, and the textural characteristics information that described texture self-adaption scan module extracts according to decoding end texture analyzer is selected a kind of counter-scanning order adapting.

7. texture self-adaption video decode system according to claim 6, is characterized in that: described textural characteristics packets of information is containing grain direction information, or described textural characteristics packets of information is containing grain direction information and texture strength information.

8. texture self-adaption video decode system according to claim 6, is characterized in that: the input signal of described decoding end texture analyzer comprise following one or more: reference image data, decoding function module output data.

9. texture self-adaption video decode system according to claim 6, it is characterized in that: Video Decoder also comprises texture self-adaption interpolating module, the textural characteristics information that described texture self-adaption interpolating module extracts according to decoding end texture analyzer, select a kind of interpolation method adapting, build sub-pix point, wherein, described textural characteristics information is grain direction information.

10. texture self-adaption video decode system according to claim 9, is characterized in that: texture self-adaption interpolating module and texture self-adaption scan module use of the same race or decoding unit textural characteristics information not of the same race to control.

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