1 November 1992 Adaptive entropy-constrained lattice vector quantization for multiresolution image coding
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Proceedings Volume 1818, Visual Communications and Image Processing '92; (1992); doi: 10.1117/12.131462
Event: Applications in Optical Science and Engineering, 1992, Boston, MA, United States
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.
© (1992) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Marc Antonini, Michel Barlaud, Thierry Gaidon, "Adaptive entropy-constrained lattice vector quantization for multiresolution image coding", Proc. SPIE 1818, Visual Communications and Image Processing '92, (1 November 1992); doi: 10.1117/12.131462; https://doi.org/10.1117/12.131462


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