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A fast SHVC coding scheme based on base layer co-located CU and cross-layer PU mode information
Author(s): Wei-Ju Chiang; Jiann-Jone Chen; Yao-Hong Tsai
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Paper Abstract

To serve users with different bandwidth environment, the JCT-VC proposed a scalable extension of HEVC (SHVC) standard, which can implement the scalability along temporal, spatial or quality dimensions. It encode one video to one based layer (BL) and several enhancement layer (EL) bitstreams to provide scalable video consumption. To speed up the coding process, the SHVC can additionally utilize interlayer coding mode cor- relations, as compared to the HEVC that utilizes spatial, temporal, and inter-depth coding mode correlations. In this research, we investigate coding mode correlations from SHVC code-streams and find out extensive and general inter-layer mode correlations rules. Based on these extensive and general rules, we proposed two fast coding methods: (1) To fast encode one EL CU, it refers to the co-located BL CU depth to reduce the number of coding depth tests. The high and low speedup approaches adopt general but poor quality rules and extensive but good quality rules, respectively; (2) To fast encode EL PUs, the co-located BL PU modes and inter-layer mode correlations and classifications are used to specify required PU modes for test. Experiments showed that the proposed fast SHVC methods that combines the fast CU and fast PU coding procedures can reduce 76.71%and 62.7%, respectively, of processing time with the high and low speedup approaches.

Paper Details

Date Published: 29 October 2018
PDF: 7 pages
Proc. SPIE 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence, 1083611 (29 October 2018); doi: 10.1117/12.2504630
Show Author Affiliations
Wei-Ju Chiang, National Taiwan Univ. of Science and Technology (Taiwan)
Jiann-Jone Chen, National Taiwan Univ. of Science and Technology (Taiwan)
Yao-Hong Tsai, Hsuan Chuang Univ. (Taiwan)

Published in SPIE Proceedings Vol. 10836:
2018 International Conference on Image and Video Processing, and Artificial Intelligence
Ruidan Su, Editor(s)

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