A Progressive Universal Noiseless Coder

Michelle Effros; Robert M. Gray; Eve A. Riskin

doi:10.1109/18.272460

A Progressive Universal Noiseless Coder

Michelle Effros
, Robert M. Gray
, Eve A. Riskin

Department of Electrical and Computer Engineering

Research output: Contribution to journal › Article › peer-review

5 Scopus citations

Abstract

We combine pruned tree-structured vector quantization (pruned TSVQ) with Itoh’s universal noiseless coder. By combining pruned TSVQ with universal noiseless coding, we benefit from the “successive approximation" capabilities of TSVQ, thereby allowing progressive transmission of images, while retaining the ability to noiselessly encode images of unknown statistics in a provably asymptotically optimal fashion. Noiseless compression results are comparable to Ziv-Lempel and arithmetic coding for both images and finely quantized Gaussian sources.

Original language	English
Pages (from-to)	108-117
Number of pages	10
Journal	IEEE Transactions on Information Theory
Volume	40
Issue number	1
DOIs	https://doi.org/10.1109/18.272460
State	Published - Jan 1994

Keywords

Progressive transmission
medical image coding
universal noiseless coding

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10.1109/18.272460

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AU - Riskin, Eve A.

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AB - We combine pruned tree-structured vector quantization (pruned TSVQ) with Itoh’s universal noiseless coder. By combining pruned TSVQ with universal noiseless coding, we benefit from the “successive approximation" capabilities of TSVQ, thereby allowing progressive transmission of images, while retaining the ability to noiselessly encode images of unknown statistics in a provably asymptotically optimal fashion. Noiseless compression results are comparable to Ziv-Lempel and arithmetic coding for both images and finely quantized Gaussian sources.

KW - Progressive transmission

KW - medical image coding

KW - universal noiseless coding

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A Progressive Universal Noiseless Coder

Abstract

Keywords

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