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18 January 2010Phase refinement for image prediction based on sparse representation
Aurélie Martin,Jean-Jacques Fuchs,Christine Guillemot,Dominique Thoreau
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Aurélie Martin,1,2 Jean-Jacques Fuchs,1 Christine Guillemot,1 Dominique Thoreau2

1IRISA, Univ. de Rennes I (France)
2Thomson Corporate Research (France)
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
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Abstract
In this work, we propose the use of sparse signal representation techniques to solve the problem of closed-loopspatial image prediction. The reconstruction of signal in the block to predict is based on basis functions selectedwith the Matching Pursuit (MP)i terative algorithm, to best match a causal neighborhood. We evaluate this newmethod in terms of PSNR and bitrate in a H.264 / AVC encoder. Experimental results indicate an improvementof rate-distortion performance. In this paper, we also present results concerning the use of phase correlation toimprove the reconstruction trough shifted-basis functions.
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Aurélie Martin,Jean-Jacques Fuchs,Christine Guillemot, andDominique 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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Cited by 1 scholarly publication and 1 patent.
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KEYWORDS
Chemical species
Associative arrays
Reconstruction algorithms
Fourier transforms
Computer programming
Image processing
Signal detection
Wavelets
Neodymium
Reliability
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Aurélie Martin, Jean-Jacques Fuchs, Christine Guillemot, 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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