Vector quantization of subband images using entropy-weighted mean square error

Jun Liu; Tim N. Davidson

doi:10.1117/12.258248

14 November 1996 Vector quantization of subband images using entropy-weighted mean square error

Jun Liu, Tim N. Davidson

Proceedings Volume 2847, Applications of Digital Image Processing XIX; (1996) https://doi.org/10.1117/12.258248
Event: SPIE's 1996 International Symposium on Optical Science, Engineering, and Instrumentation, 1996, Denver, CO, United States

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.

Citation Download Citation

Jun Liu and Tim N. Davidson "Vector quantization of subband images using entropy-weighted mean square error", Proc. SPIE 2847, Applications of Digital Image Processing XIX, (14 November 1996); https://doi.org/10.1117/12.258248

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