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A convex programming bit allocation method for sparse sources

Abstract : The objective of this paper is to design an efficient bit allocation algorithm in the subband coding context based on an analytical approach. More precisely, we consider the uniform scalar quantization of subband coefficients modeled by a Generalized Gaussian distribution. This model appears to be particularly well-adapted for data having a sparse representation in the wavelet domain. Our main contribution is to reformulate the bit allocation problem as a convex programming one. For this purpose, we firstly define new convex approximations of the entropy and distortion functions. Then, we derive explicit expressions of the optimal quantization parameters. Finally, we illustrate the application of the proposed method to wavelet-based coding systems.
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Contributor : Mounir Kaaniche Connect in order to contact the contributor
Submitted on : Friday, September 21, 2012 - 10:21:29 PM
Last modification on : Saturday, January 15, 2022 - 3:56:49 AM
Long-term archiving on: : Saturday, December 22, 2012 - 3:39:10 AM


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  • HAL Id : hal-00734456, version 1


Mounir Kaaniche, Aurélia Fraysse, Beatrice Pesquet-Popescu, Jean-Christophe Pesquet. A convex programming bit allocation method for sparse sources. Proceedings of the IEEE International Picture Coding Symposium (PCS), May 2012, Poland. pp.853-856. ⟨hal-00734456⟩



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