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Proceedings Paper

Vector quantization of subband images using entropy-weighted mean square error
Author(s): Jun Liu; Tim N. Davidson
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Paper Abstract

A new distortion measure, entropy-weighted mean square error, is introduced to enhance the perceptual quality of reconstructed images form subband vector quantization schemes. The measure is based on the observation that the subbands containing more information ought to be more accurately represented than those which contain less. A compatible feature extractor for a non-linear interpolative vector quantization scheme is proposed in order to extend the method to higher dimensional vector spaces without incurring an excessive computational burden. The experimental results confirm the predictions of improved perceptual quality.

Paper Details

Date Published: 14 November 1996
PDF: 8 pages
Proc. SPIE 2847, Applications of Digital Image Processing XIX, (14 November 1996); doi: 10.1117/12.258248
Show Author Affiliations
Jun Liu, McMaster Univ. (Canada)
Tim N. Davidson, McMaster Univ. (Canada)

Published in SPIE Proceedings Vol. 2847:
Applications of Digital Image Processing XIX
Andrew G. Tescher, Editor(s)

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