WIT Press


Optimal Entropy Encoders For Mining Multiply Resolved Data

Price

Free (open access)

Paper DOI

10.2495/DATA000071

Volume

25

Pages

10

Published

2000

Size

1,299 kb

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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