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Proceedings Paper

A optimized context-based adaptive binary arithmetic coding algorithm in progressive H.264 encoder
Author(s): Guang Xiao; Xu-li Shi; Ping An; Zhao-yang Zhang; Ge Gao; Guo-wei Teng
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Paper Abstract

Context-based Adaptive Binary Arithmetic Coding (CABAC) is a new entropy coding method presented in H.264/AVC that is highly efficient in video coding. In the method, the probability of current symbol is estimated by using the wisely designed context model, which is adaptive and can approach to the statistic characteristic. Then an arithmetic coding mechanism largely reduces the redundancy in inter-symbol. Compared with UVLC method in the prior standard, CABAC is complicated but efficiently reduce the bit rate. Based on thorough analysis of coding and decoding methods of CABAC, This paper proposed two methods, sub-table method and stream-reuse methods, to improve the encoding efficiency implemented in H.264 JM code. In JM, the CABAC function produces bits one by one of every syntactic element. Multiplication operating times after times in the CABAC function lead to it inefficient.The proposed algorithm creates tables beforehand and then produce every bits of syntactic element. In JM, intra-prediction and inter-prediction mode selection algorithm with different criterion is based on RDO(rate distortion optimization) model. One of the parameter of the RDO model is bit rate that is produced by CABAC operator. After intra-prediction or inter-prediction mode selection, the CABAC stream is discard and is recalculated to output stream. The proposed Stream-reuse algorithm puts the stream in memory that is created in mode selection algorithm and reuses it in encoding function. Experiment results show that our proposed algorithm can averagely speed up 17 to 78 MSEL higher speed for QCIF and CIF sequences individually compared with the original algorithm of JM at the cost of only a little memory space. The CABAC was realized in our progressive h.264 encoder.

Paper Details

Date Published: 12 May 2006
PDF: 8 pages
Proc. SPIE 6246, Visual Information Processing XV, 62460S (12 May 2006); doi: 10.1117/12.665371
Show Author Affiliations
Guang Xiao, Shanghai Municipal Educational Examinations Authority (China)
Xu-li Shi, Ministry of Education (China)
Ping An, Shanghai Univ. (China)
Zhao-yang Zhang, Ministry of Education (China)
Ge Gao, Shanghai Univ. of Engineering Science (China)
Guo-wei Teng, Central Research Academy, SVA (GROUP) Co. Ltd (China)

Published in SPIE Proceedings Vol. 6246:
Visual Information Processing XV
Zia-ur Rahman; Stephen E. Reichenbach; Mark A. Neifeld, Editor(s)

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