DocumentCode :
34612
Title :
Enhanced Inter-Prediction Via Shifting Transformation in the H.264/AVC
Author :
Blasi, Saverio G. ; Peixoto, E. ; Izquierdo, Ebroul
Author_Institution :
Sch. of Electron. Eng. & Comput. Sci., Queen Mary Univ. of London, London, UK
Volume :
23
Issue :
4
fYear :
2013
fDate :
Apr-13
Firstpage :
735
Lastpage :
740
Abstract :
Inter-prediction based on block-based motion estimation (ME) is used in most video codecs. The closer the prediction to the target block, the lower the residual, and thus more efficient compression can be achieved. In this paper, a new technique called enhanced inter-prediction (EIP) is proposed to improve the prediction candidates using an additional transformation acting while performing ME. A parametric transformation acts within the coding loop of each block to modify the prediction for each motion vector candidate. The EIP is validated in the particular case of a single-parameter shifting transformation. This paper presents an efficient algorithm to compute the best shift for each prediction candidate and a model to select the optimal prediction based on minimum cost integrating the approach with existing rate-distortion optimization techniques in the H.264/AVC video codec. Results show significant improvements with an average of 6% bit-rate reduction compared to the original H.264/AVC.
Keywords :
data compression; error statistics; motion estimation; prediction theory; rate distortion theory; video codecs; video coding; EIP; H.264/AVC video codec; ME; bit-rate reduction; block-based motion estimation; coding loop; compression; enhanced inter-prediction; enhanced interprediction; motion vector; optimal prediction; parametric transformation; rate-distortion optimization technique; single-parameter shifting transformation; target block prediction; Decoding; Encoding; Gain; PSNR; Standards; Vectors; Video coding; H.264/AVC; inter-prediction; video coding;
fLanguage :
English
Journal_Title :
Circuits and Systems for Video Technology, IEEE Transactions on
Publisher :
ieee
ISSN :
1051-8215
Type :
jour
DOI :
10.1109/TCSVT.2012.2214931
Filename :
6279460
Link To Document :
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