Applying Data Mining Techniques in a Wyner-Ziv to H.264 Video Transcoder

被引:0
|
作者
Luis Martinez, Jose [1 ]
Corrales-Garcia, Alberto [2 ]
Cuenca, Pedro [2 ]
Jose Quiles, Francisco [2 ]
机构
[1] Univ Complutense, Architecture & Technol Comp Syst Group, E-28040 Madrid, Spain
[2] Univ Castilla La Mancha, Inst Invest Informat Albacete I3A, Albacete 02071, Spain
关键词
Distributed Video Coding; Wyner-Ziv; H.264; Data Mining; Machine Learning; Transcoding;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper proposes an improved Wyner-Ziv to H.264 transcoder for supporting mobile-to-mobile video communications. In this framework, both transmitter and receptor should employ video encoders and decoders of low complexity. Taking advantage of both paradigms, in terms of low complexity algorithms, a suitable solution consists in transcoding from Wyner-Ziv to H.264. In order to reduce this process this paper proposes an algorithm which is based on the hypothesis that macroblock coding mode decisions in H.264 video have a high correlation with the distribution of the side information residual in Wyner-Ziv video. The proposed algorithm, which is based on data mining techniques, selects one sub-set of the several coding modes in H.264. Simulation results show that the proposed transcoder reduces the inter prediction complexity in H.264 by up to 53%, while maintaining coding efficiency.
引用
收藏
页码:497 / 504
页数:8
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