Anisotropic multiscale sparse learned bases for image compression

Angélique Drémeau; Cédric Herzet; Christine Guillemot; Jean-Jacques Fuchs

doi:10.1117/12.838691

18 January 2010 Anisotropic multiscale sparse learned bases for image compression

Angélique Drémeau, Cédric Herzet, Christine Guillemot, Jean-Jacques Fuchs

Proceedings Volume 7543, Visual Information Processing and Communication; 754304 (2010) https://doi.org/10.1117/12.838691
Event: IS&T/SPIE Electronic Imaging, 2010, San Jose, California, United States

Abstract

This paper proposes a new compression algorithm based on multi-scale learned bases. We first explain the construction of a set of image bases using a bintree segmentation and the optimization procedure used to select the image basis from this set. We then present the sparse orthonormal transforms introduced by Sezer et al.¹ and propose some extensions tending to improve the convergence of the learning algorithm on the one hand and to adapt the transforms to the coding scheme used on the other hand. Comparisons in terms of rate-distortion performance are finally made with the current compression standards JPEG and JPEG2000.

Citation Download Citation

Angélique Drémeau, Cédric Herzet, Christine Guillemot, and Jean-Jacques Fuchs "Anisotropic multiscale sparse learned bases for image compression", Proc. SPIE 7543, Visual Information Processing and Communication, 754304 (18 January 2010); https://doi.org/10.1117/12.838691

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