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Title: Fast mode decision algorithm for HEVC screen content intra coding
Authors: Kuang, W 
Tsang, SH 
Chan, YL 
Siu, WC 
Issue Date: 2017
Source: In Proceedings of 2017 IEEE International Conference on Image Processing (ICIP), 17-20 September 2017, Beijing, China, p. 2473-2477
Abstract: Screen Content coding (SCC) is one of an extension to High Efficiency Video Coding (HEVC) developed by the Joint Collaborative Team on Video Coding (JCT-VC). It adopts two new coding tools, intra block copy (IBC) and palette (PLT) modes, to improve the compression performance for intra coding. Nevertheless, mode selection causes a substantial increase in encoding complexity. In this paper, a fast mode decision algorithm, which makes use of early mode skip decision based on the Bayesian decision rule using online learning, is proposed. The proposed algorithm is implemented in the SCC reference software SCM-7.0. Experimental results show that the proposed algorithm can achieve 23.2% complexity reduction on average with only 0.58% Bjontegaard delta bitrate loss in All Intra (AI) configurations.
Keywords: Bayesian decision rule
Fast mode decision
High Efficiency Video Coding (HEVC)
Screen content coding (SCC)
Publisher: Institute of Electrical and Electronics Engineers
ISBN: 978-1-5090-2175-8 (Electronic)
978-1-5090-2176-5 (Print on Demand(PoD))
DOI: 10.1109/ICIP.2017.8296727
Description: 2017 IEEE International Conference on Image Processing (ICIP), 17-20 September 2017, Beijing, China
Rights: ©2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
The following publication W. Kuang, S. -H. Tsang, Y. -L. Chan and W. -C. Siu, "Fast mode decision algorithm for HEVC screen content intra coding," 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China, 2017, pp. 2473-2477 is available at https://doi.org/10.1109/ICIP.2017.8296727.
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