Share Email Print
cover

Proceedings Paper

Entropy-constrained predictive residual vector quantization
Author(s): Syed A. Rizvi; Lin-Cheng Wang; Nasser M. Nasrabadi
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

A major problem with a VQ based image compression scheme is its codebook search complexity. Recently, a new VQ scheme called predictive residual vector quantizer (PRVQ) was proposed which has a performance very close to that of the predictive vector quantizer (PVQ) with very low search complexity. This paper presents a new variable-rate VQ scheme called entropy-constrained PRVQ (EC-PRVQ), which is designed by imposing a constraint on the output entropy of the PRVQ. We emphasized the design of EC-PRVQ for bit rates ranging from 0.2 bpp to 1.00 bpp. This corresponds to the compression ratios of 8 through 40, which are likely to be used by most of the real life applications permitting lossy compression. The proposed EC-PRVQ is found to give a good rate-distortion performance and clearly outperforms the state-of-the-art image compression algorithms developed by Joint Photographic Experts Group (JPEG). The robustness of EC-PRVQ is demonstrated by encoding several test images taken from outside the training data. EC-PRVQ not only gives better performance than JPEG, at a manageable encoder complexity, but also retains the inherent simplicity of VQ decoder.

Paper Details

Date Published: 8 December 1995
PDF: 12 pages
Proc. SPIE 2605, Coding and Signal Processing for Information Storage, (8 December 1995); doi: 10.1117/12.228223
Show Author Affiliations
Syed A. Rizvi, SUNY/Buffalo (United States)
Lin-Cheng Wang, SUNY/Buffalo (United States)
Nasser M. Nasrabadi, SUNY/Buffalo (United States)


Published in SPIE Proceedings Vol. 2605:
Coding and Signal Processing for Information Storage
Raghuveer M. Rao; Soheil A. Dianat; Steven W. McLaughlin; Martin Hassner, Editor(s)

© SPIE. Terms of Use
Back to Top