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Proceedings Paper

Adaptive vector quantization with fuzzy distortion measure for image coding
Author(s): Suryalakshmi Pemmaraju; Sunanda Mitra; L. Rodney Long; George R. Thoma; Yao-Yang Shieh; Glenn H. Roberson
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Paper Abstract

Despite the proven superiority of vector quantization (VQ) over scalar quantization (SQ) in terms of rate distortion theory, currently existing vector quantization algorithms, still, suffer from several practical drawbacks, such as codebook initialization, long search-process, and optimization of the distortion measure. We present a new adaptive vector quantization algorithm that uses a fuzzy distortion measure to find a globally optimum codebook. The generation of codebooks is facilitated by a self-organizing neural network-based clustering that eliminates adhoc assignment of the codebook size as required by standard statistical clustering. In addition, a multiresolution wavelet decomposition of the original image enhances the process of codebook generation. Preliminary results using standard monochrome images demonstrate excellent convergence of the algorithm, significant bit rate reduction, and yield reconstructed images with high visual quality and good PSNR and MSE. Extension of this adaptive VQ to color image compression is currently under investigation.

Paper Details

Date Published: 15 April 1996
PDF: 7 pages
Proc. SPIE 2707, Medical Imaging 1996: Image Display, (15 April 1996); doi: 10.1117/12.238494
Show Author Affiliations
Suryalakshmi Pemmaraju, Texas Tech Univ. (United States)
Sunanda Mitra, Texas Tech Univ. (United States)
L. Rodney Long, National Library of Medicine (United States)
George R. Thoma, National Library of Medicine (United States)
Yao-Yang Shieh, Health Sciences Ctr./Texas Tech Univ. (United States)
Glenn H. Roberson, Health Sciences Ctr./Texas Tech Univ. (United States)


Published in SPIE Proceedings Vol. 2707:
Medical Imaging 1996: Image Display
Yongmin Kim, Editor(s)

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