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Proceedings Paper

Phase refinement for image prediction based on sparse representation
Author(s): Aurélie Martin; Jean-Jacques Fuchs; Christine Guillemot; Dominique Thoreau
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Paper Abstract

In this work, we propose the use of sparse signal representation techniques to solve the problem of closed-loop spatial image prediction. The reconstruction of signal in the block to predict is based on basis functions selected with the Matching Pursuit (MP)i terative algorithm, to best match a causal neighborhood. We evaluate this new method in terms of PSNR and bitrate in a H.264 / AVC encoder. Experimental results indicate an improvement of rate-distortion performance. In this paper, we also present results concerning the use of phase correlation to improve the reconstruction trough shifted-basis functions.

Paper Details

Date Published: 18 January 2010
PDF: 8 pages
Proc. SPIE 7543, Visual Information Processing and Communication, 75430H (18 January 2010); doi: 10.1117/12.838911
Show Author Affiliations
Aurélie Martin, IRISA, Univ. de Rennes I (France)
Thomson Corporate Research (France)
Jean-Jacques Fuchs, IRISA, Univ. de Rennes I (France)
Christine Guillemot, IRISA, Univ. de Rennes I (France)
Dominique Thoreau, Thomson Corporate Research (France)

Published in SPIE Proceedings Vol. 7543:
Visual Information Processing and Communication
Amir Said; Onur G. Guleryuz, Editor(s)

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