High-efficiency parallelism solution for a Multiview High-Efficiency Video Coding decoder

Thus far, Multiview High-Efficiency Video Coding (MV-HEVC) can only use a central processing unit (CPU) to perform decompression on a personal computer (PC) or workstation. Because MV-HEVC is much more complex than High-Efficiency Video Coding (HEVC), decompressors need higher parallelism to decompr...

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Bibliographic Details
Published inJournal of parallel and distributed computing Vol. 141; pp. 115 - 128
Main Authors Liu, Wei, Li, Wei, Cho, Yong Beom
Format Journal Article
LanguageEnglish
Published Elsevier Inc 01.07.2020
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ISSN0743-7315
1096-0848
DOI10.1016/j.jpdc.2020.03.008

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Summary:Thus far, Multiview High-Efficiency Video Coding (MV-HEVC) can only use a central processing unit (CPU) to perform decompression on a personal computer (PC) or workstation. Because MV-HEVC is much more complex than High-Efficiency Video Coding (HEVC), decompressors need higher parallelism to decompress in real time. Therefore, this study presents a parallel method based on MV-HEVC. Inter-view complete parallelism is realized according to the dependent relationship between other MV-HEVC views, and a search range is not required to avoid the data dependence between frames. Based on the dependencies of each task in MV-HEVC, an advanced wavefront parallel processing method is proposed to achieve higher intra-frame parallelism. The parallel structure of the proposed method is compatible with that of the single-instruction multiple-data acceleration method. The results showed that the proposed method can decompress MV-HEVC with 20 threads in real time for 1088p video with three views. •We presented a parallel method based on Multiview High Efficiency Video Coding. An advanced WPP method is proposed to achieve higher intra-frame parallelism.•The proposed method is compatible with that of the SIMD acceleration method.•The proposed method can achieve real-time decompression of MV-HEVC with three views.
ISSN:0743-7315
1096-0848
DOI:10.1016/j.jpdc.2020.03.008