Parallel Multiview Video Coding Exploiting Group of Pictures Level Parallelism

This paper presents the use of a computer cluster with heterogeneous computing components to provide concurrency and multi-level parallelism at coarse grain and massive fine-grain for multiview video coding (MVC) applications. MVC involves coding of multiple video sequences that are taken from the s...

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Bibliographic Details
Published in:IEEE transactions on parallel and distributed systems 2016-08, Vol.27 (8), p.2316-2328
Main Authors: Caoyang Jiang, Nooshabadi, Saeid
Format: Article
Language:English
Subjects:
Acceleration
Algorithms
Central Processing Unit
cluster computing
Coding
Computational efficiency
Encoding
Graphics processing units
group of pictures
Multiview video
parallel computing
Parallel processing
Pictures
Polypropylenes
Streaming media
Video coding
Video compression
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Summary:This paper presents the use of a computer cluster with heterogeneous computing components to provide concurrency and multi-level parallelism at coarse grain and massive fine-grain for multiview video coding (MVC) applications. MVC involves coding of multiple video sequences that are taken from the same scene but different perspective. In addition to motion estimation (ME) used in conventional video coding for single view video for exploiting inter-frame temporal similarities, MVC adopts disparity estimation (DE) to further increase compression. To overcome the huge computational cost associated with ME and by extension with DE, attention has been mainly focused on developing fast ME/DE algorithms. Although fast ME/DE algorithms bring substantial speedup, to achieve realtime MVC encoding, it requires further acceleration of the coding process at higher levels. Towards this end, this paper proposes a multiple-view-parallel, multiple-interleaved group of pictures (multiple-IGOP) scheduling scheme for MVC. When evaluated over eight views, with no loss in rate distortion (RD) performance, the proposed scheme outperforms view-sequential coding by a factor of up to 12.4 and 12.3, respectively, for two popular prediction structures, IBP and IPP.
ISSN:1045-9219
1558-2183
DOI:10.1109/TPDS.2015.2485993