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Proceedings Paper

Image coding using multiresolution Markov random fields
Author(s): Michel Barlaud; Laure Blanc-Feraud; P. Charbonnier
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Paper Abstract

The purpose of this paper is to propose a new scheme for image coding from the point of view of inverse problems. The goal is to find an approximation of the image that preserves edges for a given bit rate. In order to achieve better visual quality and to save computation time, the image is first decomposed using biorthogonal wavelets. We assume that wavelet coefficient sub-images can be modeled by Markov random fields (MRF) with line process. The sub- images are then approximated so their entropy decrease and edges are preserved. Thus, the visual quality of the reconstructed image is controlled. We also look at the MRF model and a monoresolution image approximation method, along with a short overview of wavelet-based multiresolution analysis. Finally, we describe the multiresolution coding scheme and give some results.

Paper Details

Date Published: 19 May 1992
PDF: 12 pages
Proc. SPIE 1657, Image Processing Algorithms and Techniques III, (19 May 1992); doi: 10.1117/12.58310
Show Author Affiliations
Michel Barlaud, Univ. de Nice-Sophia Antipolis (France)
Laure Blanc-Feraud, Univ. de Nice-Sophia Antipolis (France)
P. Charbonnier, Univ. de Nice-Sophia Antipolis (France)


Published in SPIE Proceedings Vol. 1657:
Image Processing Algorithms and Techniques III
James R. Sullivan; Benjamin M. Dawson; Majid Rabbani, Editor(s)

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