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Spatial intra-prediction based on mixtures of sparse representations

Angélique Drémeau 1 Mehmet Türkan 1 Cédric Herzet 1 Christine Guillemot 1 Jean-Jacques Fuchs 1 
1 TEMICS - Digital image processing, modeling and communication
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : In this paper, we consider the problem of spatial prediction based on sparse representations. Several algorithms dealing with this problem can be found in the literature. We propose a novel method involving a mixture of sparse representations. We first place this approach into a probabilistic framework and then derive a practical procedure to solve it. Comparisons of the rate-distortion performance show the superiority of the proposed algorithm with regard to other state-of-the-art algorithms.
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Submitted on : Wednesday, November 24, 2010 - 9:47:58 AM
Last modification on : Thursday, February 10, 2022 - 2:08:03 PM


  • HAL Id : inria-00539118, version 1


Angélique Drémeau, Mehmet Türkan, Cédric Herzet, Christine Guillemot, Jean-Jacques Fuchs. Spatial intra-prediction based on mixtures of sparse representations. Proc. IEEE International Workshop on Multimedia Signal Processing (MMSP), Oct 2010, Saint Malo, France. ⟨inria-00539118⟩



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