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Proceedings Paper

Anisotropic multiscale sparse learned bases for image compression
Author(s): Angélique Drémeau; Cédric Herzet; Christine Guillemot; Jean-Jacques Fuchs
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Paper 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.

Paper Details

Date Published: 18 January 2010
PDF: 8 pages
Proc. SPIE 7543, Visual Information Processing and Communication, 754304 (18 January 2010); doi: 10.1117/12.838691
Show Author Affiliations
Angélique Drémeau, INRIA-Rennes Research Ctr. (France)
Cédric Herzet, INRIA-Rennes Research Ctr. (France)
Christine Guillemot, INRIA-Rennes Research Ctr. (France)
Jean-Jacques Fuchs, INRIA-Rennes Research Ctr. (France)

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

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