基于率失真优化的AVS-M关键参考帧选择算法  

Selection Algorithms of Key Reference Pictures for AVS-M Based on Rate-Distortion Optimization

作　　者：张庆明[1] 彭强[1] 代志刚[1] 

机构地区：[1]西南交通大学信息科学与技术学院,四川成都610031

出　　处：《华南理工大学学报（自然科学版）》2009年第10期44-48,65,共6页Journal of South China University of Technology(Natural Science Edition)

基　　金：国家自然科学基金资助项目(60672099)

摘　　要：AVS—M标准的差错控制技术简单，差错恢复效果不理想．文中从像素级对AVS—M视频编码传输过程中的端到端失真进行了分析，将一种传输失真度估算模型和传统率失真优化准则相结合，形成一种率失真优化模型．利用此模型实现了一种无反馈关键参考帧选择算法，提出一种在有反馈信道条件下的有反馈关键参考帧选择算法．实验结果表明：采用的失真度估计模型能够较精确地估计出端到端的失真度；在3％-20％丢包率下，无反馈和有反馈关键参考帧选择方法较AVS-M标准的Y-PSNR值分别提高了1．9—6．4dB和2．7—10．9dB，有效提高了AVS-M视频传输的抗差错性能．The error resilience of AVS-M can not achieve desirable results because the adopted error control technology is too simple. In order to solve this problem, the end-to-end error propagation distortion in AVS-M video encoding and transmission is investigated at a precision level of pixel, and a non-feedback key reference picture selection （KRPS） algorithm is realized, based on the rate-distortion optimization model which combines the general transmission distortion estimation model with the rate-distortion optimization （RDO） rule. Then, a feedback KRPS algorithm is proposed based on the non-feedback KRPS. Experimental results indicate that the adopted distortion estimation model can accurately estimate the end-to-end distortion, and that, as compared with the algorithm of AVS-M, the non-feedback and the feedback KRPS algorithms respectively result in the Y-PSNR gains of 1.9 -6. 4dB and 2. 7 ～ 10. 9dB, at a packet loss rate varying from 3% to 20%. It is thus concluded that the two proposed algorithms effectively improve the performance of error resilience in AVS-M video encoding.

关 键 词：视频编码 差错恢复 率失真优化 端到端失真 参考帧选择 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]