Research On Fast Encoding Algorithms For High Efficiency Video Coding

In order to ease the impact of dramatic growth of high-definition(HD)and ultra-highdefinition(UHD)videos to network transmission,two distinguished international standardization organizations,i.e.,ITU-T Video Coding Experts Group(VCEG)and ISO/IEC Moving Picture Experts Group(MPEG),work together to establish the latest High Efficiency Video Coding(HEVC)standard in 2013.HEVC follows the block-based hybrid video coding approach in H.264/AVC,but improves almost every coding technique to achieve 40% bit-rate reduction for equal or even better perceptual video quality relative to H.264/AVC.However,these coding techniques in turn increase significantly the computational complexity of HEVC and limit real-time HEVC encoders in applications.This paper compares systematically the key techniques in HEVC,summarizes their development courses and tendencies,and highlights fast encoding algorithms for intra coding,inter coding and transform coding.These fast algorithms are instructive for the realtime HEVC encoders in real-life applications.The main research work and contributions are listed as follows:1.It gives a comparative study on the key techniques in HEVC,and summarizes their development courses and tendencies.After the introduction to the history of HEVC,its encoding framework and structure are analyzed.Then,the development courses and tendencies of the key techniques in HEVC,including intra prediction,inter prediction,transformation,entropy coding,in-loop filters,and parallelization,are summarized.In addition,the application fields and computational complexity of all generations of video coding standards are compared.Finally,the individual computational complexity of each key technique in HEVC are analyzed.According to the backgound of the existing fast encoding algorithms,fast encoding algorithms for intra coding,inter coding and transform coding are figured out.2.To reduce the computational complexity of intra coding,a fast algorithm based on image texture analysis is proposed.The proposed algorithm exploits the texture complexity correlations among adjacent coding tree units(CTUs),as well as the relationship of directional edge textures and coding unit(CU)size decision,to determine the CU size at an early stage.Firstly,the intra prediction unit(PU)partition types,intra prediction modes,and the whole intra coding process are introduced successively,followed by the computational complexity analysis of intra coding.Then,the background of fast algorithms for intra coding are summarized.After validating the spatial correlation in adjacent images,several image texture representations are studied.According to the texture complexity correlations among current CTU and its spatial neighboring CTUs,specific CU size ranges are assigned to current CTU according to its location in the image,which avoids traversing all the CU sizes,PU sizes and intra prediction modes for current CTU.In addition,four directional edge textures and their corresponding calculation formulas are proposed.Extensive experiments are conducted to obtain the relationships between the directional edge textures and CU sizes under different thresholds,which can be used to speed up the CU size selection and skip unnecessary rate-distortion(RD)cost evaluations.Finally,experimental results show the effectiveness of the fast intra coding algorithm based on image texture analysis.3.To reduce the computational complexity of inter coding,a fast algorithm based on motion estimation(ME)and Merge mode is proposed.The proposed algorithm expliots the consistency of motion information in videos and Merge mode selection to early determine Merge mode as the best PU mode,and further speeds up the CU size decision with motion information and Merge mode.Firstly,the inter PU partition types,ME process,and the whole inter coding process are introduced successively,followed by the computational complexity analysis of inter coding.Then,the background of fast algorithms for inter coding are summarized.The impact of two ME parameters,i.e.coded block flag(CBF)and motion vector(MV),on Merge mode decision are studied,followed by the extensive experiments to determine the fast Merge mode decision condition,under which the remaining PU modes evaluations are skipped.In addition,the correlations between Merge mode and the best CU size are analyzed.By considering the Merge mode along with the two ME parameters,the fast CU size decision condition is determined by extensive experiments.Using the fast CU size decision condition,current CU is decided whether to split or not at an early stage,and unnecessary RD cost evaluations can be skipped.Finally,experimental results show the effectiveness of the fast inter coding algorithm based on ME and Merge mode.4.To reduce the computational complexity of transform coding,a fast algorithm based on spatial correlations at different levels is proposed.The proposed algorithm exploits the transform unit(TU)size correlations among adjacent CTUs,as well as sub-CUs inside one CU,to determine the TU size at an early stage.Firstly,the TU partition types,transformation,and the whole transform coefficient coding process are introduced successively,followed by the computational complexity analysis of transform coding.Then,the background of fast algorithms for transform coding are summarized.On the CTU level,based on the spatial correlation,the transform depths of spatial neighboring CTUs are used to predict that of current CTU,according to which a specific maximum transform depth is assigned for current CTU.This avoids evaluating all the TU sizes,and the corresponding transformation,and coefficient coding for each CTU.On the CU level,according to the TU size correlations among four sub-CUs generated from one CU partitioning,the TU size range of the first sub-CU is used to accelerate the best TU size selection of the remaining three sub-CUs.Therefore,a large number of unnecessary RD cost evaluations are skipped to speed up transform coding.Finally,experimental results show the effectiveness of the fast transform coding algorithm based on spatial correlations at different levels.In summary,compared with the HEVC test model,the proposed fast algorithms make more use of image texture,statistical and spatial correlations,and the improvements for intra coding,inter coding and transform coding are efficient.In other words,the encoding time and computational complexity is significantly reduced,without sacrificing much compression efficiency and video quality.Finally,the thesis is summarized,and the prospect of the future research are figured out.