A Progressive Universal Noiseless Coder

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

Research output: Contribution to journalArticlepeer-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 languageEnglish
Pages (from-to)108-117
Number of pages10
JournalIEEE Transactions on Information Theory
Volume40
Issue number1
DOIs
StatePublished - Jan 1994

Keywords

  • Progressive transmission
  • medical image coding
  • universal noiseless coding

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