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Proceedings Paper

Adaptive entropy-constrained lattice vector quantization for multiresolution image coding
Author(s): Marc Antonini; Michel Barlaud; Thierry Gaidon
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Paper Abstract

In many different fields, digitized images are replacing conventional analog images as photograph or X-rays. The volume of data required to describe such images greatly slows down transmission and makes storage prohibitively costly. The information contained in the images must therefore be compressed by extracting only the visible elements, which are then encoded. The quantity of data involved is thus substantially reduced. High compression rates can be achieved using wavelet transform and vector quantization (VQ) of wavelet coefficients subimages. In this paper, we propose a new scheme to vector quantize real Laplacian or generalized Gaussian sources using a multidimensional compandor and lattice vector quantization. We propose an approximation formula to compute the number of points contained in an n-dimensional hypercube--or truncated lattice when using uniform source data. We also propose an analytical expression for the distortion gain when a uniform source, rather than a Laplacian one, is quantized.

Paper Details

Date Published: 1 November 1992
PDF: 17 pages
Proc. SPIE 1818, Visual Communications and Image Processing '92, (1 November 1992); doi: 10.1117/12.131462
Show Author Affiliations
Marc Antonini, Univ. de Nice--Sophia Antipolis (France)
Michel Barlaud, Univ. de Nice--Sophia Antipolis (France)
Thierry Gaidon, Univ. de Nice--Sophia Antipolis (France)

Published in SPIE Proceedings Vol. 1818:
Visual Communications and Image Processing '92
Petros Maragos, Editor(s)

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