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Optical Engineering

Alphabet- and entropy-constrained vector quantization of image pyramids
Author(s): R. Padmanabha Rao; William A. Pearlman
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Paper Abstract

A recently introduced algorithm for multirate vector quantization is used for coding image pyramids. The algorithm, called alphabet- and entropy-constrained vector quantization (AECVQ), operates by optimally choosing sub-codebooks from a large generic codebook. Simulations using 1-D AR and speech samples and full-band image data have shown the performance of AECVQ to be equal to that of entropy-constrained VQ (ECVQ); however, the ECVQ,which is also the best existing vector quantizer, is a single-rate coder. Excellent results at 1 bpp and below, judged both visually and using peak-to-peak SNR criterion, have been obtained by coding image pyramids using the AECVQ algorithm. These results demonstrate significant improvements over existing schemes. Although an AECVQ-based image coding scheme is considerably complex, it can be implemented in real time using current VLSI technology.

Paper Details

Date Published: 1 July 1991
PDF: 8 pages
Opt. Eng. 30(7) doi: 10.1117/12.55891
Published in: Optical Engineering Volume 30, Issue 7
Show Author Affiliations
R. Padmanabha Rao, Rensselaer Polytechnic Institute (United States)
William A. Pearlman, Rensselaer Polytechnic Institute (United States)

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