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PRE
AI
Y
Yufen Yang
T
Tao Lin
L
Liping Zhao *
K
Kailun Zhou *
S
Shuhui Wang
DOI:delete
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摘要

摘要

En 中文
Screen contents have become a popular image type driven by the growing market for transferring display screen between devices, especially mobile devices. Due to the ultra-high quality display featured in most of nowadays mobile devices, lossless screen content coding (SCC) is usually required or preferred. Mobile devices also require ultra-low power consumption in all tasks including SCC. To address these issues, this paper proposes an ultra-low coding complexity technique based on string matching for high efficiency lossless SCC. The technique covers three major coding phases of fast searching, prediction, and entropy coding. Condensed hash table (CHT) based fast searching is proposed to speed-up reference string searching process. Coplanar prediction (CP) and predictor-dependent residual (PDR) are presented to first efficiently predict an unmatchable pixel using multiple neighboring pixels and then further reduce the entropy of prediction residuals. To achieve a good trade-off between coding complexity and efficiency, 4-bit-aligned variable length code (4bVLC) and byte-aligned multi-variable-length-code (BMVLC) are proposed to code the prediction residuals and three string matching parameters, respectively. For 184 screen content images commonly used, compared with X265 and PNG in the default configuration and lossless mode, the proposed technique achieves 35.67% less total compressed bytes with only 0.96% encoding and 1.54% decoding runtime, and 10.04% less total compressed bytes with only 6.83% encoding and 24.32% decoding runtime, respectively. The proposed technique also outperforms X265 and PNG in all other configurations. For twelve HEVC-SCC CTC images, compared with PNG in fast, default and slow configurations and X265 in ultrafast and default configurations, the proposed technique shows significant advantage with both high coding efficiency and ultra-low coding complexity.

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机构

S
shaoxing university
学者数:
5.7K
论文数: 3.7K
被引数: 88
T
tongji university
学者数:
7.8W
论文数: 6.0W
被引数: 98
引用论文

引用论文

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