JOURNAL ARTICLE

Efficient intra mode decision for low complexity HEVC screen content compression

Qiuwen ZhangYongbo ZhaoWeiwei ZhangLijun SunRijian Su

Year: 2019 Journal:   PLoS ONE Vol: 14 (12)Pages: e0226900-e0226900   Publisher: Public Library of Science

Abstract

High efficiency video coding screen content coding (HEVC-SCC) extension is the latest HEVC development to improve the compression performance of screen content (SC) video. Similar to HEVC, the intra mode selection in HEVC-SCC is performed all the coding unit (CU) partitions to find the least rate distortion (RD) cost. Furthermore, additional intra tools are introduced to improve HEVC-SCC coding efficiency. However, these new tools could cause high computation complexity which restricts HEVC-SCC from ongoing applications. To solve the problem, an efficient intra mode decision for HEVC-SCC that adaptively utilizes the texture complexity of SC treeblock is proposed. The texture complexity of a SC treeblock is first analyzed according to the variation degree of the luminance value. And then, two efficient approaches are proposed based on the constructed model, which are early CU depth level determination and adaptive intra mode selection. Experimental results demonstrate that the proposed method can save 48.5% encoder runtime while keeping nearly the same coding efficiency as the HEVC-SCC encoders.

Keywords:
Computer science Encoder Coding (social sciences) Algorithmic efficiency Context-adaptive binary arithmetic coding Algorithm Coding tree unit Computational complexity theory Data compression Decoding methods Mathematics Statistics

Metrics

5
Cited By
0.66
FWCI (Field Weighted Citation Impact)
30
Refs
0.70
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Video Coding and Compression Technologies
Physical Sciences →  Computer Science →  Signal Processing
Advanced Vision and Imaging
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Advanced Image Processing Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
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