CN107454425A - A kind of SCC intraframe codings unit candidate modes reduction method - Google Patents
A kind of SCC intraframe codings unit candidate modes reduction method Download PDFInfo
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Abstract
A kind of method of CU candidate modes reduction scope in SCC frames.Present invention utilizes the feature of screen content video and the Space Consistency of coding unit, by the feature for the gray level co-occurrence matrixes for calculating the coding unit that depth is 0 and 1, utilize adjacent encoder unit correlation feature, to the coding unit that depth is 0, predict whether to skip Intra patterns in advance, to the coding unit that depth is 1, predict whether to skip Intra and Palette patterns in advance.This method effectively can reduce to SCC intraframe coding unit candidate modes, so as to reduce the complexity of SCC encoders, on the premise of screen content video encoding quality is had little influence on, improve SCC encoder coding rates.
Description
The technical field is as follows:
the present invention relates to the field of Screen Content Coding (SCC), and more particularly, to a prediction mode decision technique for an SCC intra-frame Coding unit.
Background art:
in recent years, with the wider application of video conferences, remote desktop control and the like, people have an increasing demand for screen videos such as animation, images with characters and diagrams and the like. Screen video has characteristics different from natural video, such as discontinuous color tone, very sharp edges of text or graphics, no capturable noise, limited number of local block colors, and different inter-frame correlation. The Screen Content Video Coding (SCC) is a new technology proposed on the basis of an High Efficiency Video Coding standard (HEVC) extension framework, and adds some new Coding technologies on the basis of a Coding Unit (Coding Unit) structure of HEVC based on a quadtree, so as to improve the Screen Content Video Coding Efficiency. In order to reduce the Intra-coded spatial redundancy information, the SCC adds a number of coding techniques for Screen video in the Intra-prediction candidate mode selection process, in addition to the Intra 35 prediction modes adopted by HEVC, the added techniques include Intra Block Copy (Intra bc), Palette (Palette) mode, fastintra bc mode, Adaptive Color Transform (ACT), and the like, see in particular document 1(JCTVC-U1014, r.joshi, s.liu, j.xu, y.ye, "Screen content coding test model 5," warraw, polar and, June 2015.). The pattern mode is an encoding technique newly introduced by the SCC, for example, in document 2(Guo L, PuW, zuo F, et al, color pattern for screen content encoding. image processing (icip),2014IEEE International Conference on.ieee,2014: 5556-.
Intra-frame coding mode selection process of general test platform SCM8.0 of SCC for current largest coding unit LCU Detecting different coding unit sizes and corresponding prediction modes layer by layer from depth 0 to depth 3 according to rate distortion The cost criterion determines the optimal coding unit size and the optimal prediction mode. For a CU with a depth of 0, the Intra and IntraBCMerge mode; for a CU with a depth of 1, detecting IntraBC, Intra, IntraBCMerge and Palette sequentially A mode; for CU with depth 2, detecting IntraBC, Intra, IntraBCMerge, FastIntraBC (1DSearch) in sequence And a pattern mode; for a CU with a depth of 3, the IntraBC, the Intra, the,IntraBCMerge、FastIntraBC (1DSearch, Hash-Search) and pattern mode, and then selecting the current depth by calculating the rate-distortion Cost RD _ Cost The optimal mode of (2).Researchers have optimized the complexity of SCC intra-coding and achieved good results as document 4 using average Pixel cost to determine the size of SCC intra-coding in advance (safety K, therapy P C, Soyjaudah K m. early CU size determination in HEVC inter-prediction using average Pixel code. digital Information and Communication Technology and it's applications (dictap),2014four International Conference. ieee,2014: 247-. Document 5 proposes a fast intra-coding tree unit depth decision algorithm based on entropy and coding bit number (Zhang M, Guo Y, Bai h. fast intra partition algorithm for HEVC screen content coding and Image Processing Conference,2014 ieee,2014: 390-.
The invention content is as follows:
the invention aims to provide a method for reducing the range of candidate prediction modes of CU in an SCC frame.
The main idea of the invention is to reduce the search range of candidate prediction modes in the CU frame with depth of 0 and 1 of an SCC encoder by utilizing the characteristics of the CU gray level co-occurrence matrix and the strong correlation between adjacent CUs, thereby reducing the complexity of the SCC encoder. Specifically, for CUs with depths of 0 and 1, a gray level co-occurrence matrix of a current CU and 5 features thereof (including second Moment (ASM), Entropy (ENT), Inverse Difference Moment (IDM), autocorrelation (COR), and Moment of Inertia (Moment of Inertia, MOI)) are calculated, and features and division conditions of the gray level co-occurrence matrix of adjacent CUs are obtained, wherein the current CU is labeled as current CU, and upper, left, and upper CUs adjacent thereto are labeled as above CU, LeftCU, and above leflcu, respectively, and their adjacent relationships are shown in fig. 1. For CUs with depths of 0 and 1, the present invention defines a mode reduction FlagPM. If Flag is presentPM1, for a CU with depth 0, skip Intra mode, forAnd the depth 1 CU skips the Intra and Palette modes. If Flag is presentPM0, according to SCCIntra-frame coding mode of general test platform SCM8.0 Selection procedureAnd (6) predicting.
Wherein Flag isPMThe calculation formula of (a) is as follows:
in the formula (1), SPA、SPL、SPLAAnd respectively representing the flag bits of whether the three adjacent CUs are divided, namely the corresponding flag bit is 1 if the three adjacent CUs are divided, and is 0 if the three adjacent CUs are not divided. SPFinalCUThe partition case of the neighboring CU is most similar to the partition case of the CurrentCU, and whether the partition is performed is also distinguished by 0 and 1.
The SPFinalCUThe calculation method of (2) is as follows:
firstly, comparing the correlation between the above CU and the LeftCU and the CurrentCU, and selecting the more correlated CU as the TempCU, wherein the correlation calculation formula is as follows:
judging according to the obtained result of formula (2), if CTempCUIf the value of (1) is greater than 2, the TempCU is an AboveCU, otherwise, the TempCU is a leftCU. Wherein,andthe absolute value of the difference of the ith characteristic of the gray level co-occurrence matrix of the current CU and the adjacent CU is used for reflecting the texture similarity degree, the smaller the value of the absolute value is, the greater the similarity degree is proved,andas shown in formula (3) and formula (4),the ith features of the gray level co-occurrence matrices of CurrentCU, LeftCU and AboveCU are represented respectively.
And calculating the correlation between the TempCU obtained according to the formula and the leftabove cu and the CurrentCU, wherein the calculation formula is shown as a formula (5), the more correlated one of the TempCU and the leftabove cu is selected as Final cu, and the division condition is SP in the formula (1)FinalCU。
Wherein,
judging according to the obtained result of formula (5) if CFinalCUIf the value of (1) is greater than 2, the FinaCU is a LeftAboveCU, otherwise the FinaCU is a TempCU. Wherein,andsuch as formula (6) and formula (7),andthe ith characteristics of the gray level co-occurrence matrices for TempCU and LeftAboveCU, respectively.
By adopting the scheme, the invention has the beneficial effects that:
1. according to the method, the characteristics of similarity of characteristics of adjacent CU gray level co-occurrence matrixes and CU spatial consistency are utilized, CU prediction mode conditions with the depths of 0 and 1 are analyzed, the intra-frame prediction candidate mode range is reduced by adopting an optimization mode, and meanwhile, the prediction accuracy is guaranteed.
2. The invention comprehensively considers the characteristics of the SCC video sequence, and can effectively reduce the range of the candidate modes of the predicted CU, thereby obviously improving the SCC intra-frame coding efficiency and reducing the complexity of an SCC coder under the condition of almost no loss of coding quality.
Description of the drawings:
fig. 1 is a positional relationship of a current CU and its neighboring CUs.
Fig. 2 is a flow chart of a SCC intra mode selection fast algorithm based on texture complexity.
FIG. 3 is a diagram showing the relationship between the generation step length and the generation angle of the gray level co-occurrence matrix.
The specific implementation mode is as follows:
the method utilizes the characteristics of the screen content video and the spatial consistency of the coding units, calculates the characteristics of the gray level co-occurrence matrixes of the coding units with the depths of 0 and 1, and predicts whether to skip the Intra mode in advance for the coding unit with the depth of 0 and predict whether to skip the Intra and Palette modes in advance for the coding unit with the depth of 1 by utilizing the correlation characteristics of adjacent coding units. The method can effectively reduce the candidate prediction modes of the SCC intra-frame coding unit, thereby reducing the complexity of an SCC coder and improving the coding speed of the SCC coder on the premise of hardly influencing the video coding quality of screen contents.
The following describes and demonstrates embodiments of the present invention in conjunction with the accompanying drawings. As shown in fig. 2, the details are as follows:
step (1), after encoding of an LCU is started based on a universal test platform SCM8.0 of SCC, the depth of a current intra-frame encoding unit is firstly judged. If the depth is 0 or 1, turning to the step (2). And (4) if the depth is 2 and 3, turning to the step (4).
And (2) calculating the gray level co-occurrence matrix and the characteristics of the CU. The gray level co-occurrence matrix is a gray level joint probability distribution of a pair of pixels which are in a certain same position relation in space, the matrix is marked as PM, the probability distribution of the elements of the position matrix is marked as Pd(i, j), wherein D is the generation step length and represents the spatial position relationship between two pixels, different D determines the distance between two pixels and the generation direction theta, i and j represent two pixels, the position relationship is shown in fig. 3, x and y in the figure represent the horizontal and vertical coordinates of the pixels respectively, and Dx、DyRespectively, represent the offset. The matrix is formed as follows:
three parameters, namely a gray level L, a generating direction theta and a generating step length d, are required to be determined for constructing the gray level co-occurrence matrix, the gray level L is selected to be 64, the generating direction theta is selected to be 0 degree and 90 degrees, and the generating step length d is selected to be 8.
The selected characteristics of the gray level co-occurrence matrix are a second moment, entropy, an inverse difference moment, autocorrelation and an inertia moment, wherein the second moment is also called energy and is the sum of squares of all elements in the PM, the gray level co-occurrence matrix can reflect the gray level distribution condition of the CU, the entropy reflects the texture uniformity degree of the CU, the inverse difference moment reflects the smoothness degree of the CU, the autocorrelation reflects the similarity of the PM in a certain direction, and the inertia moment reflects the complexity of the spatial distribution of the PM. The above-mentioned feature calculation formula is as follows:
wherein h is1、h2、h3、h4、h5Respectively representing the values of ASM, ENT, IDM, COR, MOI calculated by the gray level co-occurrence matrix, wherein mu1、μ2、Respectively represent the mean value and the variance of the horizontal and vertical coordinates of the PM, and the calculation formula is as follows:
step (3), calculating a mode reduction FlagPMIf Flag is presentPM1 and the depth of the CU is 0, skipping prediction of an Intra mode, and only predicting an IntraBCMerge mode; if Flag is presentPM1 and the depth of the CU is 1, the prediction of the Intra mode and the Palette mode is skipped, and the prediction of the Intra bc mode and the Intra bcmerge mode are performed. Otherwise, detecting the intra-frame prediction mode according to the SCM8.0 standard flow, and turning to the step (5).
FlagPMThe calculation formula is as follows:
in the formula (1), SPA、SPL、SPLAAnd respectively representing the flag bits of whether the three adjacent CUs are divided, namely the corresponding flag bit is 1 if the three adjacent CUs are divided, and is 0 if the three adjacent CUs are not divided. SPFinalCUThe partition case of the neighboring CU is most similar to the partition case of the CurrentCU, and whether the partition is performed is also distinguished by 0 and 1. SPFinalCUThe calculation method of (2) is as follows:
firstly, comparing the correlation between the above CU and the LeftCU and the CurrentCU, and selecting the more correlated CU as the TempCU, wherein the correlation calculation formula is as follows:
wherein,
judging according to the obtained result of formula (2), if CTempCUIf the value of (1) is greater than 2, the TempCU is an AboveCU, otherwise, the TempCU is a leftCU. Wherein,andthe absolute value of the difference of the ith characteristic of the gray level co-occurrence matrix of the current CU and the adjacent CU is used for reflecting the texture similarity degree, the smaller the value of the absolute value is, the greater the similarity degree is proved,andas shown in formula (3) and formula (4),the ith features of the gray level co-occurrence matrices of CurrentCU, LeftCU and AboveCU are represented respectively.
And calculating the correlation between the TempCU obtained according to the formula and the leftabove cu and the CurrentCU, wherein the calculation formula is shown as a formula (5), the more correlated one of the TempCU and the leftabove cu is selected as Final cu, and the division condition is SP in the formula (1)FinalCU。
Wherein,
judging according to the obtained result of formula (5) if CFinalCUIf the value of (1) is greater than 2, the FinaCU is a LeftAboveCU, otherwise the FinaCU is a TempCU. Wherein,andsuch as formula (6) and formula (7),andthe ith characteristics of the gray level co-occurrence matrices for TempCU and LeftAboveCU, respectively.
And (4) when the CU depths are 2 and 3, detecting the intra-frame prediction mode according to the SCM8.0 standard flow, and turning to the step (5).
And (5) recursively obtaining the optimal depth and the optimal mode, and finishing the current LCU coding.
Table 1 shows the experimental results of the present algorithm and the standard SCC algorithm under the SCM8.0 platform. The test configuration selects RA and LD respectively. The QP chosen was 22, 27, 32 and 27. The resolution of the test video was 1920 × 1080 and 1280 × 720. And selecting YUV444, YUV420 and RGB444 from the test video. On average, the YUV444 format video saves about 16% of time, and the BD-Rate rises about 1.34%; the video in YUV420 format saves about 20% of time, and increases about 1.1% of BD-Rate; the video in the RGB444 format saves about 14% of time and the BD-Rate rises about 1.06%.
TABLE 1 Experimental results of the inventive and Standard SCC algorithms under SCM8.0 platform (%)
Claims (3)
1. The invention discloses a method for reducing the range of candidate prediction modes of CUs in an SCC frame, which is characterized in that the method utilizes the characteristics of a CU gray level co-occurrence matrix and strong correlation between adjacent CUs to reduce the search range of candidate prediction modes of the CUs with SCC encoder depths of 0 and 1 and reduce the complexity of an SCC encoder. Specifically, for CUs with depths of 0 and 1, a gray level co-occurrence matrix of the current CU and 5 features thereof (including An Second Moment (ASM), Entropy, Inverse Difference Moment (IDM), autocorrelation (COR), and Moment of inertia (Moment of inertia) are calculatedInertia, MOI)), and simultaneously acquiring the characteristics of the gray level co-occurrence matrix of adjacent CUs and the dividing condition, wherein the current CU is marked as CurrentCU, and the upper, left and upper left CUs adjacent to the current CU are respectively marked as above CU, LeftCU and above LeftCU. For CUs with depths of 0 and 1, the present invention defines a mode reduction FlagPM. If Flag is presentPMFor 1, Intra mode is skipped for CU with depth 0, and Intra and Palette modes are skipped for CU with depth 1. If Flag is presentPM0, according to SCCIntra-frame coding mode selection process of general test platform SCM8.0And (6) predicting.
2. The method of claim 1, wherein the Flag is set to zeroPMThe calculation formula of (a) is as follows:
in the formula (1), SPA、SPL、SPLAAnd respectively representing the flag bits of whether the three adjacent CUs are divided, namely the corresponding flag bit is 1 if the three adjacent CUs are divided, and is 0 if the three adjacent CUs are not divided. SPFinalCUThe partition case of the neighboring CU is most similar to the partition case of the CurrentCU, and whether the partition is performed is also distinguished by 0 and 1.
3. The method of claim 2, wherein the SP is a master batchFinalCUThe calculation method of (2) is as follows:
firstly, comparing the correlation between the above CU and the LeftCU and the CurrentCU, and selecting the more correlated CU as the TempCU, wherein the correlation calculation formula is as follows:
wherein,
judging according to the obtained result of formula (2), if CTempCUIf the value of (1) is greater than 2, the TempCU is an AboveCU, otherwise, the TempCU is a leftCU. Wherein,andthe absolute value of the difference of the ith characteristic of the gray level co-occurrence matrix of the current CU and the adjacent CU is used for reflecting the texture similarity degree, the smaller the value of the absolute value is, the greater the similarity degree is proved,andas shown in formula (3) and formula (4),the ith features of the gray level co-occurrence matrices of CurrentCU, LeftCU and AboveCU are represented respectively.
And calculating the correlation between the TempCU obtained according to the formula and the leftabove cu and the CurrentCU, wherein the calculation formula is shown as a formula (5), the more correlated one of the TempCU and the leftabove cu is selected as Final cu, and the division condition is SP in the formula (1)FinalCU。
Wherein,
according toThe obtained result of the formula (5) is judged if CFinalCUIf the value of (1) is greater than 2, the FinaCU is a LeftAboveCU, otherwise the FinaCU is a TempCU. Wherein,andsuch as formula (6) and formula (7),andthe ith characteristics of the gray level co-occurrence matrices for TempCU and LeftAboveCU, respectively.
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