CN112929657B - H.266/VVC rapid CU partition decision method based on gradient and variance - Google Patents
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Abstract
The invention provides a gradient and variance based H.266/VVC rapid CU partitioning method, which comprises the following steps: firstly, the homogeneity of the current CU is evaluated according to the variance value, and whether the further division of the current CU can be stopped in advance is judged. And then extracting gradient features of the current CU by using a Sobel operator, and judging whether the current CU can carry out QT division or not so as to skip BT and TT division. And finally, extracting the edge characteristics of the texture of the current CU by using a Canny operator, eliminating MT division in the vertical or horizontal division direction according to the trend of the texture of the current CU, taking MT division in the other direction as a candidate, and taking the division mode with the minimum RDO-cost as an optimal division mode. The invention makes a decision on the CU division step by step, accelerates the CU division process through early termination and early skipping, obviously reduces the complexity of CU division and greatly improves the coding efficiency under the condition of ensuring the coding quality.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to an H.266/VVC fast CU partition decision method based on gradient and variance.
Background
With the development of multimedia devices and the popularization of high-quality videos, especially after ultra High Definition (HDR) videos are more widely applied, the conventional video coding standard h.265/HEVC can no longer effectively meet the video compression requirements of high quality and low bitrate. In order to meet the requirements of high-quality video application and development, a new generation of video coding standard h.266/VVC is established and gradually perfected, and the huge data volume and coding complexity along with the new generation of video coding standard hinder further development and application of video coding technology.
In order to reduce the complexity of intra-frame prediction in H.266/VVC and improve the coding efficiency on the premise of ensuring almost unchanged video quality, the invention designs a rapid method for partitioning a coding tree unit (CU). CU partitioning is the basis of intra prediction or even the entire video compression coding process. H.266/VV introduces a multi-branch tree partition structure, adds four CU partition modes and adds an asymmetric CU partition structure. CU partitioning evolves from single, fixed partitioning towards flexible partitioning approaches. The evolution of the CU partition structure is beneficial to more accurately partitioning the image structure and more efficiently adapting to image coding and decoding with different resolutions and different characteristics. However, the coding quality is improved, and meanwhile, huge computational complexity is brought, and higher requirements are put forward on coding and decoding. Therefore, when designing fast algorithms, it is often necessary to strike a balance between coding quality and coding efficiency for practical application scenarios or algorithm trends.
Many fast CU partitioning methods have been proposed to speed up the coding process of CU partitioning. All the methods can achieve different degrees of coding time reduction, and simultaneously control the distortion of the compressed video within a certain range. However, these algorithms usually have a significant short board, and often cannot achieve a good balance between coding quality and coding efficiency.
Research on fast CU partitioning methods has been developed. Fan et al propose a gradient method based on texture complexity and a gradient-based preprocessing step to reduce the number of intra mode candidates and make early decisions for CU partitioning. Min et al propose a texture-based fast CU partitioning algorithm, in which the edge complexity of a CU in the vertical, horizontal, 45 ° and 135 ° diagonal directions is used as an important texture feature to determine CU partitioning. Shen uses the temporal and spatial information as conditions for determining the CU depth range. CU depths that are used less frequently will be skipped or terminated early. Ruiz et al propose a direction-based algorithm that utilizes local direction variance along a predetermined line. The direction with the lowest directional variance is considered the dominant direction, so the number of intra mode candidates that require Rate Distortion Optimization (RDO) processing is correspondingly reduced. Wang et al uses the intra prediction mode of the neighboring CU to adaptively select a small set of candidates to enter the RDO process. Lee et al studied the relationship between the impossible mode of the current CU and the distortion distribution of Sub-coding Units (Sub-Code Units, Sub-CUs) to skip or terminate unnecessary partitions. Sun proposes a preprocessing method based on the complexity of the texture extracted from the macroblock and the directional energy distribution along a specific direction. According to the texture complexity, an early termination strategy for CU partition is proposed. Wang et al introduced the Otsu method to measure the texture complexity of each LCU, skipping some CU depth levels according to the texture complexity. Meanwhile, a modified Sobel operator is applied to measure the gradient direction of the PU to reduce some candidate intra modes.
Disclosure of Invention
Aiming at the defects in the background art, the invention provides an H.266/VVC fast CU partition decision method based on gradient and variance, and solves the technical problems of large coding time expenditure and poor rate-distortion control of the existing fast CU partition technology.
The technical scheme of the invention is realized as follows:
a gradient and variance based H.266/VVC fast CU partition decision method comprises the following steps:
the method comprises the following steps: calculating the variance of the current CU, and comparing the variance of the current CU with a set threshold value T h 1, comparing, if the variance of the current CU is less than the threshold value T h 1, the texture of the current CU is homogeneous, the current CU is not divided, and otherwise, the step two is executed;
step two: extracting absolute gradients of all pixels in the current CU and the total gradient of the current CU by using a Sobel operator, and setting a threshold value T h 2 and T h 3, and judging whether the absolute gradient and the total gradient satisfy a threshold value T h 2 and T h 3, if the judgment condition is met, executing a quadtree partitioning mode by the current CU, otherwise, executing a step three;
step three: extracting an edge feature image of the current CU by using a Canny operator, judging whether the texture trend of the current CU is a horizontal texture trend or a vertical texture trend through the edge feature image, if the texture trend of the current CU is the horizontal texture trend, vertically dividing the multi-branch tree into candidate division modes of the current CU, and executing the step four, otherwise, judging that the texture trend of the current CU is the vertical texture trend, horizontally dividing the multi-branch tree into the candidate division modes of the current CU, and executing the step five;
step four: respectively calculating rate distortion optimization loss values of binary tree vertical partitions and ternary tree vertical partitions contained in candidate partition mode multi-branch tree vertical partitions, and selecting the candidate partition mode with the minimum rate distortion optimization loss value as the optimal partition mode of the current CU;
step five: and respectively calculating rate distortion optimization loss values of binary tree horizontal division and ternary tree horizontal division contained in the candidate division mode multi-branch tree horizontal division, and selecting the candidate division mode with the minimum rate distortion optimization loss value as the optimal division mode of the current CU.
The method for calculating the variance of the current CU comprises the following steps:
where V is the variance of the current CU, W is the width of the current CU, H is the height of the current CU, μ is the average of all pixels in the current CU, and X (i, j) is the pixel value with coordinates (i, j) in the current CU.
The absolute gradient of all pixels in the current CU is:
wherein, F x (i, j) is the absolute gradient in the horizontal direction, F y (i, j) is the absolute gradient in the vertical direction, A i,j Is the original pixel matrix centered at coordinate (i, j) with a step size of 3.
The total gradient of the current CU is:
wherein, F X Total gradient in horizontal direction, F Y Abs (-) is a function of absolute value for the total gradient in the vertical direction.
The threshold T h 2 and T h The judgment condition constituted by 3 is: 1) f X /F Y <T h 2 or F Y /F X <T h 2，2)T h 3<F X and T h 3<F Y 。
The method for extracting the edge feature image of the current CU by using the Canny operator comprises the following steps:
wherein ve i Is the texture value in the vertical direction, he i Is a texture value in the horizontal direction, V E For vertical grain trends, H E For horizontal texture trend, can (x, y) represents the pixel value of coordinate (x, y) in the edge feature image of the current CU, max (·) is a maximum function, and min (·) is a minimum function.
Judging condition for judging texture trend of current CU through edge feature image is H E /V E When H is present E /V E <When 1, the texture trend of the current CU is a horizontal texture trend, and when H is E /V E >1, the texture trend of the current CU is a vertical texture trend.
Compared with the prior art, the invention has the following beneficial effects: the method judges the attribute of the texture of the CU by using the variance and gradient characteristics extracted by the Sobel operator and the Canny operator, accordingly divides the dividing process of the CU into three steps which are gradually and progressively advanced, realizes the early termination or skipping of unnecessary dividing modes, effectively reduces the complexity, has almost unchanged Rate Distortion (RD) performance, and achieves good balance between the coding efficiency and the coding quality.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a comparison of the overall coding time savings of the present invention compared to the EQBP, FCPD, FPDS methods;
FIG. 3 is a comparison of the BDBR increase of the present invention with the EQBP, FCPD, FPDS methods;
FIG. 4 shows the comparison result of the overall saving of encoding time and the comparison result of the increase of BDBR compared with the CNN method according to the present invention;
FIG. 5 is a graph comparing the relationship between the rate distortion and the PSNR of the rendering view of the video based on "BQTerace" in the method of the present invention and VTM 7.0;
FIG. 6 is a graph comparing the relationship between the rate distortion and the PSNR of the rendering view of the video based on "Kristen AndSare" in the present invention and VTM7.0 method;
FIG. 7 is a graph comparing rate distortion and PSNR of a rendering view based on PartScene video according to the method of the present invention and VTM 7.0;
FIG. 8 is a graph comparing rate distortion and rendering view PSNR of the present invention and VTM7.0 method based on 'BlowingBubbles' video.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art based on the embodiments of the present invention without inventive step, are within the scope of the present invention.
As shown in fig. 1, an embodiment of the present invention provides a gradient and variance based h.266/VVC fast CU partition decision method, which uses variance and gradient to determine the properties of CU texture, so as to implement early termination or skipping of unnecessary partition modes, and speed up the CU partition process while ensuring the coding quality. The invention provides three methods, namely early termination of CU partition based on variance, early skip of MT partition based on gradient features extracted by Sobel operators and determination of partition direction based on Canny operators. The method comprises the following specific steps:
the method comprises the following steps: early termination of CU partitioning based on variance. Calculating the variance of the current CU, and comparing the variance of the current CU with a set threshold value T h 1, comparing, if the variance of the current CU is less than the threshold value T h 1, the texture of the current CU is homogeneous, the current CU is not divided, and otherwise, the step two is executed;
and reflecting the performance of the whole texture according to the variance and the gradient, and selecting the variance as an index to evaluate and judge the homogeneity of the current CU. If the current CU is homogeneous, further partitioning of the current CU is terminated. For this purpose, a threshold value T is set h 1, variance of current CU is less than threshold T h When 1, the current CU texture is considered homogeneous, and the five partition modes are terminated early.
The method for calculating the variance of the current CU comprises the following steps:
where V is the variance of the current CU, W is the width of the current CU, H is the height of the current CU, μ is the average of all pixels in the current CU, and X (i, j) is the pixel value with coordinates (i, j) in the current CU.
For threshold value T h The choice of 1 is determined by analyzing the test results of 10 different types of video sequences. Obtaining a threshold range according to the test of the VTM7.0 anchor method, and continuously adjusting T in the range h A value of 1 was tested. Found in the test to be either T h How 1 changes within the range has a limited effect on the encoding time. The coding quality is taken as a main reference index for selecting a threshold value, and the point 10QP at which the coding quality starts to be remarkably reduced is selected as T h 1。
Step two: extracting absolute gradients of all pixels in the current CU and the total gradient of the current CU by using a Sobel operator, and setting a threshold value T h 2 and T h 3, and judging whether the absolute gradient and the total gradient satisfy a threshold value T h 2 and T h 3, if the judgment condition is met, executing a Quad Tree (QT) partitioning mode by the current CU, otherwise, executing a third step;
MT partition is skipped early based on gradient features extracted by Sobel operator. And extracting absolute gradients of all pixels in the CU and the total gradient of the current CU by using a Sobel operator, and judging the texture characteristics of the current CU according to the gradient trend.
The absolute gradient of all pixels in the current CU is:
wherein, F x (i, j) is the absolute gradient in the horizontal direction, F y (i, j) is the absolute gradient in the vertical direction, A i,j Is the original pixel matrix centered at coordinate (i, j) with a step size of 3.
The total gradient of the current CU is:
wherein, F X Total gradient in horizontal direction, F Y Abs (-) is a function of absolute value for the total gradient in the vertical direction.
Setting two thresholds T h 2 and T h 3, the larger one of the total gradients in the horizontal and vertical directions is used to make a quotient with the other value if the ratio is smaller than T h 2, and the total gradient values in two directions are both greater than T h 3, i.e. satisfies the threshold T h 2 and T h The judgment condition formed by 3 is: 1) f X /F Y <T h 2 or F Y /F X <T h 2，2)T h 3<F X and T h 3<F Y Then QT partition is performed skipping MT partition early.
Step three: extracting an edge feature image of the current CU by using a Canny operator, judging whether the texture trend of the current CU is a Horizontal texture trend or a Vertical texture trend through the edge feature image, if the texture trend of the current CU is the Horizontal texture trend, dividing a Multi-Tree Vertical (MT _ V) into candidate division modes of the current CU, and executing a step four, otherwise, if the texture trend of the current CU is the Vertical texture trend, dividing a Multi-Tree Horizontal (MT _ H) into candidate division modes of the current CU, and executing a step five;
a method for block-based Canny operator determination of partition direction. The traditional Canny algorithm is improved, a non-maximum inhibition process and a dual-threshold selection process in the traditional Canny algorithm are eliminated, and isolation inhibition is used for replacing the two key steps. The working steps of the block-based Canny algorithm are as follows:
1) smoothing the input image using gaussian filtering;
2) calculating gradient values and directions of all pixels;
3) setting all contour pixel values with the gray value larger than 20 in the current CU to be 0;
4) the pixel values of no contour pixels within the current CU are set to 0.
The method for extracting the edge feature image of the current CU by using the Canny operator comprises the following steps:
wherein ve i Is the texture value in the vertical direction, he i Is a texture value in the horizontal direction, V E For vertical grain trends, H E For the horizontal texture tendency, can (x, y) represents the pixel value of the coordinate (x, y) in the edge feature image of the current CU, max (·) is a maximum function, and min (·) is a minimum function.
Judging condition for judging texture trend of current CU through edge feature image is H E /V E When H is present E /V E <When 1, the texture trend of the current CU is a horizontal texture trend, and when H is E /V E >1, the texture trend of the current CU is a vertical texture trend.
Step four: respectively calculating rate-distortion optimization loss values of Binary Tree Vertical (BT _ V) partitions and Ternary Tree Vertical (TT _ V) partitions contained in the candidate partition modes MT _ V, and selecting the candidate partition mode (BT _ V or TT _ V) with the minimum rate-distortion optimization loss value (RDO-cost) as the best partition mode of the current CU;
step five: rate-distortion optimization loss values of Binary Tree level (BT _ H) partitions and Ternary Tree level (TT _ H) partitions contained in the candidate partition pattern MT _ H are calculated, respectively, and the candidate partition pattern (BT _ H or TT _ H) with the smallest rate-distortion optimization loss value is selected as the best partition pattern of the current CU.
To evaluate the performance of the proposed method, the invention tests the experiment on the official encoder VTM7.0, performing a simulation experiment using 6 sets of 13 universal test sequences. These test sequences include 1920 × 1080, 832 × 480, 416 × 240, 1280 × 720 of four different resolutions. The test conditions were as follows: and starting a full frame mode by adopting a general test condition.
The coding performance of the proposed ensemble scheme of the present invention, which can greatly save the coding run time, has a similar RD performance to the original VTM7.0, is listed in table 1. Figure 1 illustrates the workflow of the present invention. Table 2 shows the performance comparison of E, B, C and D four groups of test video sequences with other image feature correlation-based fast CU partitioning methods in the present invention, and the comparison results are visually shown in fig. 2 and fig. 3. As shown in fig. 2 and 3, the present invention is superior to the other three methods in terms of time saving and rate distortion performance, and achieves a good balance between encoding time and encoding quality. Table 3 shows the performance comparison of E, B, C and F test video sequences in the present invention and the CNN-based fast CU partitioning method, and the data results are shown in fig. 4. As shown in fig. 4, the coding time of the present invention is much shorter than that of the CNN method under almost the same rate-distortion premise. Fig. 5, fig. 6, fig. 7, fig. 8 show the RD performance of four representative different resolution video sequences based on "bqtrerace", "KristenAndSara", "PartScene", and "blowingbunbles" in the present invention and VTM7.0, respectively. Compared to VTM7.0, the present invention can achieve consistent runtime savings from a low bit rate range to a high bit rate, and with nearly similar RD performance. In general, the method of the present invention has the best coding time saving, whether for the conventional algorithm based on image feature correlation or for the CNN algorithm, and the average BDBR loss is negligible.
Table 1 encoding results of the present invention and the original encoder
TABLE 2 coding results of the present invention and other fast CU partition methods based on image feature correlation
Table 3 coding results of the present invention and CNN fast method
The invention uses a fast CU partition decision method based on variance and gradient to gradually analyze and judge the texture attribute of the H.266/VVC current CU and skip unnecessary partition modes, so as to effectively reduce the complexity of the encoder, and simultaneously, the RD performance loss is negligible.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and should not be taken as limiting the scope of the present invention, which is intended to cover any modifications, equivalents, improvements, etc. within the spirit and scope of the present invention.
Claims (4)
1. A gradient and variance based H.266/VVC fast CU partition decision method is characterized by comprising the following steps:
the method comprises the following steps: calculating the variance of the current CU, and comparing the variance of the current CU with a set threshold value T h 1, if the variance of the current CU is less than the threshold value T h 1, the texture of the current CU is homogeneous, the current CU is not divided, and otherwise, the step two is executed;
step two: extracting absolute gradients of all pixels in the current CU and the total gradient of the current CU by using a Sobel operator, and setting a threshold value T h 2 and T h 3, and judging whether the absolute gradient and the total gradient satisfy a threshold value T h 2 and T h 3: 1) f X /F Y <T h 2 or F Y /F X <T h 2，2)T h 3<F X and T h 3<F Y (ii) a If 1) and 2) are simultaneously met, executing a quadtree partitioning mode by the current CU, and otherwise, executing a step three;
the absolute gradient of all pixels in the current CU is:
wherein, F x (i, j) is the absolute gradient in the horizontal direction, F y (i, j) is the absolute gradient in the vertical direction, A i,j Is an original pixel matrix with coordinates (i, j) as the center and step length of 3;
the total gradient of the current CU is:
wherein, F X Total gradient in horizontal direction, F Y For the total gradient in the vertical direction, abs (-) is an absolute value function, W represents the width of the current CU, and H represents the height of the current CU;
step three: extracting an edge feature image of the current CU by using a Canny operator, judging whether the texture trend of the current CU is a horizontal texture trend or a vertical texture trend through the edge feature image, if the texture trend of the current CU is the horizontal texture trend, vertically dividing the multi-branch tree into candidate division modes of the current CU, and executing the step four, otherwise, judging that the texture trend of the current CU is the vertical texture trend, horizontally dividing the multi-branch tree into the candidate division modes of the current CU, and executing the step five;
step four: respectively calculating rate distortion optimization loss values of binary tree vertical partitions and ternary tree vertical partitions contained in candidate partition mode multi-branch tree vertical partitions, and selecting the candidate partition mode with the minimum rate distortion optimization loss value as the optimal partition mode of the current CU;
step five: and respectively calculating rate distortion optimization loss values of binary tree horizontal division and ternary tree horizontal division contained in the candidate division mode multi-branch tree horizontal division, and selecting the candidate division mode with the minimum rate distortion optimization loss value as the optimal division mode of the current CU.
2. The gradient and variance based h.266/VVC fast CU partition decision method according to claim 1, wherein the variance of the current CU is calculated by:
where V is the variance of the current CU, W is the width of the current CU, H is the height of the current CU, μ is the average of all pixels in the current CU, and X (i, j) is the pixel value with coordinates (i, j) in the current CU.
3. The H.266/VVC fast CU partition decision method based on gradient and variance as claimed in claim 2, wherein the method for extracting the edge feature image of the current CU by Canny operator is:
wherein ve i Is the texture value in the vertical direction, he i Is a texture value in the horizontal direction, V E For vertical grain trends, H E For horizontal texture trend, can (x, y) represents the pixel value of coordinate (x, y) in the edge feature image of the current CU, max (·) is a maximum function, and min (·) is a minimum function.
4. According to claim 3The H.266/VVC rapid CU division decision method based on gradient and variance is characterized in that the judgment condition for judging the texture trend of the current CU through the edge feature image is H E /V E When H is present E /V E <When 1, the texture trend of the current CU is a horizontal texture trend, and when H is E /V E >1, the texture trend of the current CU is a vertical texture trend.
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