DocumentCode :
3184916
Title :
Error Resilient Motion Estimation and Mode Decision for Robust H.264/AVC Video Transmission
Author :
Lie, Wen-Nung ; Gao, Zhi-Wei
Author_Institution :
Nat. Chung Cheng Univ., Minsyong
fYear :
2007
fDate :
10-12 Dec. 2007
Firstpage :
413
Lastpage :
416
Abstract :
Due to temporal prediction, via motion vectors, adopted in most video coding standards, video quality is substantially degraded at decoder side as a result of error propagation. In order to enhance robustness of existing temporal-prediction-based techniques, one prediction strategy, called error resilient motion estimation (ERME), to take both coding efficiency and error propagation into considerations is proposed in this paper. A similar concept is applied to the problem of mode decision for H.264/AVC videos, called error resilient mode decision (ERMD), so as to choose an optimal mode (intra-prediction or variable block sizes for motion estimation) for robust video transmission. Experiment results show that the integration of these two algorithms really enhances the robustness of H.264/AVC videos and outperforms traditional algorithms by 1~7 dB at a packet loss rate of 15%.
Keywords :
Pareto optimisation; code standards; error correction codes; minimisation; motion estimation; multimedia communication; rate distortion theory; video coding; video communication; ERME prediction strategy; H.264-AVC video transmission; Pareto multiobjective optimization; end-to-end distortion minimization; error propagation; error resilient mode decision; error resilient motion estimation; video coding standards; Automatic voltage control; Computational complexity; Decoding; Degradation; Motion estimation; Nonlinear distortion; PSNR; Robustness; Video coding; Video compression;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Multimedia Workshops, 2007. ISMW '07. Ninth IEEE International Symposium on
Conference_Location :
Beijing
Print_ISBN :
9780-7695-3084-0
Type :
conf
DOI :
10.1109/ISM.Workshops.2007.75
Filename :
4476004
Link To Document :
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