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dc.titleInformation theory, wavelets, and image compression
dc.contributor.authorLawton, W.
dc.identifier.citationLawton, W. (1996). Information theory, wavelets, and image compression. International Journal of Imaging Systems and Technology 7 (3) : 180-190. ScholarBank@NUS Repository.
dc.description.abstractWe 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.
dc.contributor.departmentINSTITUTE OF SYSTEMS SCIENCE
dc.description.sourcetitleInternational Journal of Imaging Systems and Technology
Appears in Collections:Staff Publications

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