CN107454425B - A kind of SCC intraframe coding unit candidate modes reduction method - Google Patents
A kind of SCC intraframe coding unit candidate modes reduction method Download PDFInfo
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- H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
- H04N19/102—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or selection affected or controlled by the adaptive coding
- H04N19/103—Selection of coding mode or of prediction mode
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- H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
- H04N19/169—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding
- H04N19/182—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being a pixel
Abstract
A kind of method of CU candidate modes reduction range in SCC frame.Present invention utilizes the Space Consistencies of the feature of screen content video and coding unit, the feature of the gray level co-occurrence matrixes for the coding unit for being 0 and 1 by calculating depth, utilize adjacent encoder unit correlation feature, the coding unit for being 0 to depth, it predicts whether to skip Intra mode in advance, the coding unit for being 1 to depth, predicts whether to skip Intra and Palette mode in advance.This method can effectively reduce SCC intraframe coding unit candidate modes, to reduce the complexity of SCC encoder, under the premise of having little influence on screen content video encoding quality, improve SCC encoder coding rate.
Description
Technical field:
The present invention relates to screen contents to encode the field (Screen Content Coding, SCC), in particular in SCC frame
The prediction mode decision-making technic of coding unit.
Background technique:
In recent years, with video conference, long- distance tabletop control etc. application it is more and more extensive, people for as animation, have
The demand of the screen videos such as the image of text chart is increasing.Screen video has the characteristics of different from natural video frequency, such as
Tone is discontinuous, text or graphic edge are very sharp keen, does not have trappable noise, localized mass number of colors limited and interframe
Correlation difference etc..Screen content Video coding (Screen Content Video Coding, SCC) is based on efficient video
The new technology proposed on coding standard (High Efficiency Video Coding, HEVC) extension framework, it is in HEVC
Some new coding techniques are added on coding unit (Coding Unit) structure basis based on quaternary tree, to improve screen
Curtain audio content code efficiency.In order to reduce intraframe coding spatial redundancy information, SCC was selected in intra prediction candidate pattern
Journey also adds many coding techniques for being directed to screen video, increases in the frame used by the HEVC in addition to 35 kinds of prediction modes
Technology include intra block duplication (Intra Block Copy, IntraBC), palette (Palette) mode,
FastIntraBC mode, adaptive color transformation (Adaptive Colour Transform, ACT) etc., referring specifically to document 1
(JCTVC-U1014,R.Joshi,S.Liu,J.Xu,Y.Ye,"Screen content coding test model 5,"
Warsaw,Poland,June 2015.).Palette mode is the coding techniques that SCC is newly introduced, such as document 2 (Guo L, Pu
W,Zou F,et al.Color palette for screen content coding.Image Processing(ICIP),
2014IEEE International Conference on.IEEE, 2014:5556-5560.), no matter to damaging or lossless
Coding, Palette mode can all significantly improve the efficiency of screen content Video coding, but the introducing of the mode also increases SCC
The complexity of intraframe coding.
The intra-frame encoding mode selection process of the general-utility test platform SCM8.0 of SCC is to current maximum coding unit LCU
From depth 0 to depth 3, successively different coding unit size and corresponding prediction mode are detected, and are distorted generation according to rate
Valence criterion determines optimal coding unit size and optimal prediction modes.For depth be 0 CU, successively detect Intra and
IntraBCMerge mode；The CU for being 1 for depth successively detects IntraBC, Intra, IntraBCMerge and Palette
Mode；The CU for being 2 for depth successively detects IntraBC, Intra, IntraBCMerge, FastIntraBC (1DSearch)
With Palette mode；The CU for being 3 for depth successively detects IntraBC, Intra, IntraBCMerge, FastIntraBC
Then (1DSearch, Hash-Search) and Palette mode chooses current depth by calculating rate distortion costs RD_Cost
Optimization model.Researcher is optimized the complexity of SCC intraframe coding at present, and achieves good effect
Fruit, as document 4 determined in advance using mean pixel cost SCC intraframe coding size (Saurty K, Catherine P C,
Soyjaudah K M.Early CU size determination in HEVC intra prediction using
Average Pixel Cost.Digital Information and Communication Technology and it's
Applications(DICTAP),2014 Fourth International Conference on.IEEE,2014:247-
252.).Document 5 then proposes a kind of quick intraframe coding tree unit depth decision making algorithm based on entropy and number of coded bits
(Zhang M,Guo Y,Bai H.Fast intra partition algorithm for HEVC screen content
coding.Visual Communications and Image Processing Conference,2014 IEEE.IEEE,
2014:390-393.)。
Summary of the invention:
The purpose of the present invention is to provide a kind of methods of CU candidate modes reduction range in SCC frame.
Main thought of the invention is to be contracted using the strong correlation between the feature and adjacent C U of CU gray level co-occurrence matrixes
Subtract candidate modes search range in the CU frame that SCC Encoder Depth is 0 and 1, reduces SCC encoder complexity.Specifically,
The CU for being 0 and 1 for depth calculates the gray level co-occurrence matrixes and its 5 feature (including second moment (Angular of current CU
Second Moment, ASM), entropy (Entropy, ENT), inverse difference moment (Inverse Different Moment, IDM), from phase
Close (Correlation, COR), the moment of inertia (Moment of Inertia, MOI)), while obtaining the gray scale symbiosis square of adjacent C U
The feature and dividing condition of battle array, wherein current CU is labeled as CurrentCU, upper, left, upper left CU adjacent thereto are marked respectively
For AboveCU, LeftCU, AboveLeftCU, neighbouring relations are as shown in Figure 1.The CU for being 0 and 1 to depth, present invention definition
Mode reduces sign of flagPM.If FlagPMIt is 1, the CU for being 0 to depth skips Intra mode, the CU for being 1 to depth,
Skip Intra and Palette mode.If FlagPMIt is 0, then according to the intra-frame encoding mode of SCC general-utility test platform SCM8.0
Select process prediction.
Wherein, FlagPMCalculation formula it is as follows:
In formula (1), SPA、SPL、SPLAThe flag bit whether three adjacent C U divide is respectively represented, i.e., if divided
Then corresponding flag bit is 1, is otherwise 0.SPFinalCUIt is the division feelings of the adjacent C U most like with CurrentCU dividing condition
Whether condition is also distinguished with 0 and 1 and is divided.
The SPFinalCUCalculation method it is as follows:
Firstly, comparing the correlation of AboveCU and LeftCU and CurrentCU, a CU more relevant in the two is selected
It is set to TempCU, correlation calculations formula is as follows:
Wherein,
Judged according to the obtained result of formula (2), if CTempCUValue be greater than 2, then TempCU be
Otherwise AboveCU is LeftCU.Wherein,WithRepresent current CU and adjacent C U gray level co-occurrence matrixes ith feature
Absolute value of the difference, for reflecting texture similarity degree, value is smaller, proves that similarity degree is bigger,WithCalculating such as
Shown in formula (3) and formula (4),Respectively represent the gray scale symbiosis square of CurrentCU, LeftCU and AboveCU
The ith feature of battle array.
The TempCU obtained according to above-mentioned formula calculates the correlation with CurrentCU, meter with LeftAboveCU again
Formula is calculated for example shown in formula (5), and select it is more relevant in the two be set to FinalCU, dividing condition is in formula (1)
SPFinalCU。
Wherein,
Judged according to the obtained result of formula (5), if CFinalCUValue be greater than 2, then FinalCU be then
Otherwise LeftAboveCU is TempCU.Wherein,WithCalculating such as formula (6) and formula (7),WithRespectively
Represent the ith feature of the gray level co-occurrence matrixes of TempCU and LeftAboveCU.
Using the above scheme, the beneficial effects of the present invention are:
1. the characteristics of present invention utilizes the similitude of the feature of adjacent C U gray level co-occurrence matrixes and CU Space Consistencies,
The CU prediction mode situation for being 0 and 1 to depth is analyzed, and optimal way is taken to reduce intra prediction candidate pattern range,
Guarantee the accuracy of prediction simultaneously.
2. the present invention has comprehensively considered the characteristic of SCC video sequence, it can effectively reduce the candidate pattern model of prediction CU
It encloses, so as in the case where hardly loss coding quality, significantly improve SCC intraframe coding efficiency, reduces SCC encoder
Complexity.
Detailed description of the invention:
Fig. 1 is the positional relationship of current CU CU adjacent thereto.
Fig. 2 is the flow chart of the SCC intra mode decision fast algorithm based on Texture complication.
The relational graph that Fig. 3 gray level co-occurrence matrixes generate step-length, generate angle.
Specific embodiment:
Present invention utilizes the Space Consistencies of the feature of screen content video and coding unit, are 0 by calculating depth
With the feature of the gray level co-occurrence matrixes of 1 coding unit, adjacent encoder unit correlation feature, the coding for being 0 to depth are utilized
Unit predicts whether to skip Intra mode in advance, the coding unit for being 1 to depth, predict whether to skip in advance Intra and
Palette mode.This method can effectively reduce SCC intraframe coding unit candidate modes, to reduce SCC
The complexity of encoder improves SCC encoder coding speed under the premise of having little influence on screen content video encoding quality
Degree.
The specific embodiment of the invention is described below in conjunction with attached drawing and provides verifying.As shown in Fig. 2, details are as follows:
Step (1) is started after encoding a LCU, based on the test platform SCM8.0 that SCC is general for compiling in present frame
Code unit, first determines whether its depth.If depth is 0 or 1, (2) are gone to step.If depth is 2,3, (4) are gone to step.
Step (2) calculates the gray level co-occurrence matrixes and its feature of CU.Gray level co-occurrence matrixes are that statistics is spatially in certain
The gray scale joint probability distribution of a pair of of pixel of kind same location relationship, matrix are denoted as PM, location matrix element probability distribution
It is denoted as Pd(i, j), wherein d is to generate step-length, indicates that the spatial relation between two pixels, different d determine two pictures
The distance between vegetarian refreshments and direction θ is generated, i, j represent two pixels, and positional relationship is as shown in figure 3, x, y generation respectively in figure
The transverse and longitudinal coordinate of table pixel, Dx、DyRespectively represent offset.The matrix of composition is as follows:
Gray level co-occurrence matrixes are constructed it needs to be determined that three parameters, i.e. gray level L, generate direction θ, generation step-length d, the present invention
Gray level L is chosen for 64, generates direction θ and is chosen for 0 degree and 90 degree, generates step-length d and is chosen for 8.
Some features of gray level co-occurrence matrixes react the complex situations of the texture of various sizes of CU, what the present invention chose
Feature has second moment, entropy, inverse difference moment, auto-correlation, the moment of inertia, and wherein second moment is also known as energy, is the flat of all elements in PM
Fang He, it can react the intensity profile situation of CU, and entropy has reacted the texture uniformity coefficient of CU, and inverse difference moment has reacted the smooth of CU
Degree, auto-correlation react the similarity of PM in a certain direction, and the moment of inertia has reacted the complexity of the spatial distribution of PM.The above institute
The feature calculation formula stated is as follows:
Wherein h1、h2、h3、h4、h5The value of gray level co-occurrence matrixes ASM, ENT, IDM, COR, MOI calculated is respectively represented,
Wherein, μ1、μ2、The mean value and variance of PM transverse and longitudinal coordinate are respectively represented, their calculation formula is as follows:
Step (3) calculates mode and reduces sign of flagPMIf FlagPMDepth for 1 and CU is 0, then skips Intra
IntraBCMerge mode is only predicted in the prediction of mode；If FlagPMDepth for 1 and CU is 1, then skip Intra mode and
Prediction IntraBC mode and IntraBCMerge mode are carried out in the prediction of Palette mode.Otherwise, according to SCM8.0 normal stream
Journey detects intra prediction mode, goes to step (5).
FlagPMCalculation formula is as follows:
In formula (1), SPA、SPL、SPLAThe flag bit whether three adjacent C U divide is respectively represented, i.e., if divided
Then corresponding flag bit is 1, is otherwise 0.SPFinalCUIt is the division feelings of the adjacent C U most like with CurrentCU dividing condition
Whether condition is also distinguished with 0 and 1 and is divided.SPFinalCUCalculation method it is as follows:
Firstly, comparing the correlation of AboveCU and LeftCU and CurrentCU, a CU more relevant in the two is selected
It is set to TempCU, correlation calculations formula is as follows:
Wherein,
Judged according to the obtained result of formula (2), if CTempCUValue be greater than 2, then TempCU be
Otherwise AboveCU is LeftCU.Wherein,WithRepresent current CU and adjacent C U gray level co-occurrence matrixes ith feature
Absolute value of the difference, for reflecting texture similarity degree, value is smaller, proves that similarity degree is bigger,WithCalculating such as
Shown in formula (3) and formula (4),Respectively represent the gray scale symbiosis square of CurrentCU, LeftCU and AboveCU
The ith feature of battle array.
The TempCU obtained according to above-mentioned formula calculates the correlation with CurrentCU, meter with LeftAboveCU again
Formula is calculated for example shown in formula (5), and select it is more relevant in the two be set to FinalCU, dividing condition is in formula (1)
SPFinalCU。
Wherein,
Judged according to the obtained result of formula (5), if CFinalCUValue be greater than 2, then FinalCU be then
Otherwise LeftAboveCU is TempCU.Wherein,WithCalculating such as formula (6) and formula (7),WithRespectively
Represent the ith feature of the gray level co-occurrence matrixes of TempCU and LeftAboveCU.
Step (4) detects intra prediction mode according to SCM8.0 normal process, goes to step (5) when CU depth is 2,3.
Step (5), recurrence obtain optimal depth and optimization model, current LCU end-of-encode.
Table 1 is experimental result of the SCC algorithm of inventive algorithm and standard under SCM8.0 platform.Test configurations are selected respectively
Take RA and two kinds of LD.The QP of selection is 22,27,32 and 27.The resolution ratio of test video is 1920 × 1080 and 1280 × 720.
Test video chooses tri- kinds of YUV444, YUV420, RGB444.On average, the format video of YUV444, time save about
16%, BD-Rate rise about 1.34%；The video of YUV420 format, time save about 20%, BD-Rate and rise about
1.1%；The video of RGB444 format, time save about 14%, BD-Rate and rise about 1.06%.
The experimental result (%) of 1 inventive algorithm of table and the SCC algorithm of standard under SCM8.0 platform
Claims (2)
1. a kind of method of CU candidate modes reduction range in SCC frame, which is characterized in that utilize CU gray level co-occurrence matrixes
Candidate modes search for model in the CU frame that strong correlation reduction SCC Encoder Depth between feature and adjacent C U is 0 and 1
It encloses, reduces SCC encoder complexity；
Specifically, the CU for being 0 and 1 for depth, calculates the gray level co-occurrence matrixes and its 5 features of current CU, including second order
Square, entropy, inverse difference moment, auto-correlation, the moment of inertia, while the feature and dividing condition of the gray level co-occurrence matrixes of adjacent C U are obtained,
In current CU be labeled as CurrentCU, upper, left, upper left CU adjacent thereto be respectively labeled as AboveCU, LeftCU,
AboveLeftCU；The CU for being 0 and 1 to depth defines mode reduction sign of flagPM；
If FlagPMIt is 1, the CU for being 0 to depth skips Intra mode, and the CU for being 1 to depth skips Intra and Palette
Mode；
If FlagPMIt is 0, then according to the intra-frame encoding mode selection process prediction of SCC general-utility test platform SCM8.0；
The FlagPMCalculation formula it is as follows:
In formula (1), SPA、SPL、SPLARespectively represent the flag bit whether three adjacent C U divide, i.e., it is corresponding if dividing
Flag bit be 1, be otherwise 0；SPFinalCUIt is the dividing condition of the adjacent C U most like with CurrentCU dividing condition, also uses
Whether 0 and 1 differentiation divides.
2. the method as described in claim 1, which is characterized in that the SPFinalCUCalculation method it is as follows:
Firstly, comparing the correlation of AboveCU and LeftCU and CurrentCU, selects a CU more relevant in the two and be set to
TempCU, correlation calculations formula are as follows:
Judged according to the obtained result of formula (2), if CTempCUValue be greater than 2, then TempCU be AboveCU, it is no
It is then LeftCU；Wherein,WithRepresent the absolute of current CU and the difference of adjacent C U gray level co-occurrence matrixes ith feature
Value, for reflecting texture similarity degree, value is smaller, proves that similarity degree is bigger,WithCalculating such as formula (3) and
Shown in formula (4),Respectively represent i-th of the gray level co-occurrence matrixes of CurrentCU, LeftCU and AboveCU
Feature；
The TempCU obtained according to above-mentioned formula calculates the correlation with CurrentCU with LeftAboveCU again, calculates public
Shown in formula such as formula (5), and select it is more relevant in the two be set to FinalCU, dividing condition is in formula (1)
DPFinalCU；
Judged according to the obtained result of formula (5), if CFinalCUValue be greater than 2, then FinalCU be then
Otherwise LeftAboveCU is TempCU；Wherein,WithCalculating such as formula (6) and formula (7),WithRespectively
Represent the ith feature of the gray level co-occurrence matrixes of TempCU and LeftAboveCU；
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