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Information theory, wavelets, and image compression
Author(s) -
Lawton Wayne
Publication year - 1996
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/(sici)1098-1098(199623)7:3<180::aid-ima4>3.0.co;2-4
Subject(s) - wavelet , wavelet transform , image compression , computer science , artificial intelligence , algorithm , mathematics , pattern recognition (psychology) , computer vision , image (mathematics) , image processing
We examine practical, theoretical, and speculative aspects of 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. © 1996 John Wiley & Sons, Inc.