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A Codeword Classification Mapping Based CAVLC Decoding Implement Algorithm

Authors: Mafei Liu; Xuewen Zeng; Hong Ni;

A Codeword Classification Mapping Based CAVLC Decoding Implement Algorithm

Abstract

The property of H.264/AVC CAVLC(context-adaptive variable-length coding) and the characteristic of Huffman Coding are analyzed, and a high efficiency implement algorithm of CAVLC decoding based on codeword classification mapping is proposed in this paper. According to the characteristic of Huffman Coding, mapping the codewords and it's syntax element to classification mapped tables, accelerate the process of CAVLC decoding enormously at the expense of little memory consumption. The simulation results show that the proposed algorithm achieves an approximate 24-50% speed advance without degrading video quality as compared to the conventional CAVLC decoding.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Top 10%
Average
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