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Proceedings Paper

Scalable image compression using combined wavelet transform and vector quantization
Author(s): Sethuraman Panchanathan; A. Jain; N. Gamaz
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Paper Abstract

In this paper, we propose vector quantization (VQ) based scheme for scalable image compression. Scalability is a generic feature referring to image representation in different sizes (spatial scalability) and/or picture qualities (SNR scalability). VQ is a powerful technique for low bit rate image compression. However the conventional VQ approach does not provide a scalable bitstream. We propose a VQ based algorithm (SVQ) to achieve spatial scalability. In SVQ technique, a pyramidal structure of three layers is built by applying 2D wavelet downsampling filters on the input image. The label stream is then made scalable such that a smaller-size image can be obtained by decoding a portion of the bitstream. This image can be further enhanced in size by progressively decoding the remaining bits. This algorithm ensures partial decodability of VQ labels by using separate codebooks one for each spatial resolution. We then propose a combination of wavelet transform and SVQ technique called WSVQ to exploit the cross-correlations among wavelet sub-bands. Simulation results confirm the substantial reductions in bit rate and superior subjective image quality at each spatial resolution using the proposed algorithm, at a significantly reduced computational complexity.

Paper Details

Date Published: 17 April 1995
PDF: 11 pages
Proc. SPIE 2419, Digital Video Compression: Algorithms and Technologies 1995, (17 April 1995); doi: 10.1117/12.206393
Show Author Affiliations
Sethuraman Panchanathan, Univ. of Ottawa (Canada)
A. Jain, Univ. of Ottawa (Canada)
N. Gamaz, Univ. of Ottawa (Canada)

Published in SPIE Proceedings Vol. 2419:
Digital Video Compression: Algorithms and Technologies 1995
Arturo A. Rodriguez; Robert J. Safranek; Edward J. Delp, Editor(s)

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