Phase refinement for image prediction based on sparse representation

Aurélie Martin; Jean-Jacques Fuchs; Christine Guillemot; Dominique Thoreau

doi:10.1117/12.838911

18 January 2010 Phase refinement for image prediction based on sparse representation

Aurélie Martin, Jean-Jacques Fuchs, Christine Guillemot, Dominique Thoreau

Proceedings Volume 7543, Visual Information Processing and Communication; 75430H (2010) https://doi.org/10.1117/12.838911
Event: IS&T/SPIE Electronic Imaging, 2010, San Jose, California, United States

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.

Citation Download Citation

Aurélie Martin, Jean-Jacques Fuchs, Christine Guillemot, and Dominique Thoreau "Phase refinement for image prediction based on sparse representation", Proc. SPIE 7543, Visual Information Processing and Communication, 75430H (18 January 2010); https://doi.org/10.1117/12.838911

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