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Proceedings Paper

Application Of Entropy-Constrained Vector Quantization To Waveform Coding Of Images
Author(s): P. A. Chou
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Paper Abstract

An algorithm recently introduced to design vector quantizers for optimal joint performance with entropy codes is applied to waveform coding of monochrome images. Experiments show that when such entropy-constrained vector quantizers (ECVQs) are followed by optimal entropy codes, they outperform standard vector quantizers (VQs) that are also followed by optimal entropy codes, by several dB at equivalent bit rates. Two image sources are considered in these experiments: twenty-five 256x 256 magnetic resonance (MR) brain scans produced by a General Electric Signa at Stanford University, and six 512 x 512 (luminance component) images from the standard USC image database. The MR images are blocked into 2 x 2 components, and the USC images are blocked into 4 x 4 components. Both sources are divided into training and test sequences. Under the mean squared error distortion measure, entropy-coded ECVQ shows an improvement over entropy-coded standard VQ by 3.83 dB on the MR test sequence at 1.29 bit/pixel, and by 1.70 dB on the USC test sequence at 0.40 bit/pixel. Further experiments, in which memory is added to both ECVQ and VQ systems, are in progress.

Paper Details

Date Published: 1 November 1989
PDF: 9 pages
Proc. SPIE 1199, Visual Communications and Image Processing IV, (1 November 1989); doi: 10.1117/12.970107
Show Author Affiliations
P. A. Chou, AT&T Bell Laboratories (United States)

Published in SPIE Proceedings Vol. 1199:
Visual Communications and Image Processing IV
William A. Pearlman, Editor(s)

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