Alphabet- and entropy-constrained vector quantization of image pyramids

R. Padmanabha Rao; William A. Pearlman

doi:10.1117/12.55891

1 July 1991 Alphabet- and entropy-constrained vector quantization of image pyramids

R. Padmanabha Rao, William A. Pearlman

Author Affiliations +

Optical Engineering, Vol. 30, Issue 7, (July 1991). https://doi.org/10.1117/12.55891

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.

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R. Padmanabha Rao and William A. Pearlman "Alphabet- and entropy-constrained vector quantization of image pyramids," Optical Engineering 30(7), (1 July 1991). https://doi.org/10.1117/12.55891

Published: 1 July 1991

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