Optimal Entropy Encoders For Mining Multiply Resolved Data
Price
Free (open access)
Volume
25
Pages
10
Published
2000
Size
1,299 kb
Paper DOI
10.2495/DATA000071
Copyright
WIT Press
Author(s)
R.A. DeVore, L.S. Johnson, C. Pan & R.C. Sharpley
Abstract
A prototype client-server implementation of image analysis and compression is described which is based on the recently developed theory of Cohen, Dahmen, DeVore, and Daubechies for optimal entropy data encoders. The class of algorithms resulting from this theory was developed for the analysis and synthesis of data and yields optimal (in an information-theoretic sense), progressive, universal encoders for purposes of compression, storage, and transmission of data which can be developed into a multi-resolution framework. Such data include photographic and sensor images, digital terrain maps, and multidimensional scientific data generated by computational simulators. Two versions of the tree encoder have been implemented with a common client interface in order to demonstrate the a
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