Paper Info
Title
Information Theory, Wavelets, And Image Compression
Abstract
We examine practical, theoretical, and speculative aspects oi wavelet transform-based image compression. Section I summarizes objectives and compares experimental results using a JPEG-standard cosine-based algorithm with a wavelet based algorithm developed at ISS. Section II analyzes image compression requirements using information theory to explain why wavelet transform-based image compression works well. The wavelet transform is shown to be a simple transform that effectively exploits second-order image statistics. Section III speculates about next-generation image compression and pattern recognition. It outlines a research plan to develop a probabilistic image model that incorporates higher-order image statistics by using wavelet expansions to provide a convergent series of finite dimensional marginal image probability densities. Physicists have successfully used similar cell cluster expansions to analyze lattice fields, Ising models, and Euclidean quantum fields. (C) 1996 John Wiley & Sons, Inc.
Year
DOI
Venue
1996
10.1002/(SICI)1098-1098(199623)7:3<180::AID-IMA4>3.3.CO;2-P
INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY
Keywords
Field
DocType
image compression,information theory
Computer vision,Harmonic wavelet transform,Computer science,Algorithm,Discrete wavelet transform,Artificial intelligence,Stationary wavelet transform,Wavelet packet decomposition,Fractal transform,Image compression,Wavelet transform,Wavelet
Journal
Volume
Issue
ISSN
7
3
0899-9457
Citations 
PageRank 
References 
0
0.34
0
Authors
1
Name
Order
Citations
PageRank
Wayne Lawton100.34