GPU-Based Hierarchical Motion Estimation for High Efficiency Video Coding

doi:10.1109/TMM.2018.2867260

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	GPU-Based Hierarchical Motion Estimation for High Efficiency Video Coding
	Wang, Shanshe 3; Luo, Falei 3,4,5; Gao, Wen 3; Ma, Siwei 3; Zhang, Xinfeng 1; Wang, Shiqi 2
刊名	IEEE TRANSACTIONS ON MULTIMEDIA
	2019-04-01
卷号	21 期号:4 页码:851-862
关键词	GPU motion estimation High Efficiency Video Coding
ISSN号	1520-9210
DOI	10.1109/TMM.2018.2867260
英文摘要	Motion estimation (ME) plays a crucial role in removing the temporal redundancy for video compression. However, during the encoding process a substantial computational burden is imposed by ME due to the exhaustive evaluations of possible candidates within the searching window. In view of the increasing computing capacity of GPU, we propose a GPU-based low delay parallel ME scheme for high efficiency video coding (HEVC). In particular, considering the quadtree coding structure of HEVC, we achieve the parallelization in a hierarchical way by optimizing the ME process in a coding tree unit (CTU), prediction unit (PU), and motion vector (MV) layers. Specifically, in the CTU layer, a novel motion vector predictor determination scheme is proposed to alleviate the side effects of inaccurate MV prediction due to the removal of the CTU-level dependency. In the PU layer, a novel indexing table is particularly designed to realize an efficient cost derivation strategy. As such, the cost of each PU can be computed in a convenient and efficient manner. In an MV layer, we propose a compact descriptor to represent MV and its corresponding cost as a whole, such that the redundant branches can be further avoided in the searching process. With such an optimization strategy, the proposed scheme can completely save the encoding time for ME on CPU. Experimental results demonstrate that the proposed scheme can achieve 41% encoding time savings with the ME acceleration up to 12.7 times, and the incurred BD-BR loss is only 0.52% on average. Moreover, further experimental results show that the proposed GPU-based ME can achieve up to 200 times acceleration compared to the full search ME on CPU.
资助项目	National Basic Research Program of China (973 Program)[2015CB351800] ; National Natural Science Foundation of China[61632001] ; National Postdoctoral Program for Innovative Talents[BX201600006] ; Top-Notch Young Talents Program of China ; Hong Kong RGC Early Career Scheme[9048122 (CityU 21211018)] ; City University of Hong Kong[7200539/CS] ; NVIDIA NVAIL program ; Shenzhen Peacock Plan
WOS研究方向	Computer Science ; Telecommunications
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000462413700004
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/4147]
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Ma, Siwei
作者单位	1.Univ Southern Calif, Ming Hsieh Dept Elect Engn, Los Angeles, CA 90089 USA 2.City Univ Hong Kong, Dept Comp Sci, Kowloon, Hong Kong 999077, Peoples R China 3.Peking Univ, Sch Elect Engn & Comp Sci, Natl Engn Lab Video Technol, Beijing 100871, Peoples R China 4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 5.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Wang, Shanshe,Luo, Falei,Gao, Wen,et al. GPU-Based Hierarchical Motion Estimation for High Efficiency Video Coding[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2019,21(4):851-862.
APA	Wang, Shanshe,Luo, Falei,Gao, Wen,Ma, Siwei,Zhang, Xinfeng,&Wang, Shiqi.(2019).GPU-Based Hierarchical Motion Estimation for High Efficiency Video Coding.IEEE TRANSACTIONS ON MULTIMEDIA,21(4),851-862.
MLA	Wang, Shanshe,et al."GPU-Based Hierarchical Motion Estimation for High Efficiency Video Coding".IEEE TRANSACTIONS ON MULTIMEDIA 21.4(2019):851-862.