Share Email Print

Proceedings Paper

Variable-rate predictive residual vector quantization
Author(s): Syed A. Rizvi; Nasser M. Nasrabadi; Lin-Cheng Wang
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 Predictive Residual Vector Quantizer (PRVQ) was proposed in Ref. 8. This scheme has a very low search complexity and its performance is very close to that of the Predictive Vector Quantizer (PVQ). This paper presents a new VQ scheme called Variable-Rate PRVQ (VR-PRVQ) which is designed by imposing a constraint on the output entropy of the PRVQ. The proposed VR-PRVQ is found to give an excellent rate-distortion performance and clearly outperforms the state-of-the-art image compression algorithm developed by Joint Photographic Experts Group (JPEG).

Paper Details

Date Published: 21 April 1995
PDF: 12 pages
Proc. SPIE 2501, Visual Communications and Image Processing '95, (21 April 1995); doi: 10.1117/12.206758
Show Author Affiliations
Syed A. Rizvi, SUNY/Buffalo (United States)
Nasser M. Nasrabadi, SUNY/Buffalo (United States)
Lin-Cheng Wang, SUNY/Buffalo (United States)

Published in SPIE Proceedings Vol. 2501:
Visual Communications and Image Processing '95
Lance T. Wu, Editor(s)

© SPIE. Terms of Use
Back to Top