Share Email Print
cover

Optical Engineering

Entropy-constrained predictive residual vector quantization
Author(s): Syed A. Rizvi; Nasser M. Nasrabadi; Lin-Cheng Wang
Format Member Price Non-Member Price
PDF $20.00 $25.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

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

Paper Details

Date Published: 1 January 1996
PDF: 11 pages
Opt. Eng. 35(1) doi: 10.1117/1.600889
Published in: Optical Engineering Volume 35, Issue 1
Show Author Affiliations
Syed A. Rizvi, U.S. Army Research Lab (United States)
Nasser M. Nasrabadi, U.S. Army Research Lab. (United States)
Lin-Cheng Wang, SUNY/Buffalo (United States)


© SPIE. Terms of Use
Back to Top