Nearly lossless vector quantization algorithm for compression of remotely sensed images

Research output: Contribution to journalConference article

Abstract

Most compression algorithms are desired for minimizing a squared error criterion. The squared error criterion does not accurately represent the fidelity requirements for scientific image compression. In this paper we propose a distortion measure which correlates with subjective evaluations, and an adaptive vector quantization algorithm which minimizes this distortion measure. A new approach to codebook design is presented to replace the nearest neighbor approach.

Original languageEnglish (US)
Pages (from-to)1247-1250
Number of pages4
JournalConference Record of the Asilomar Conference on Signals, Systems and Computers
Volume2
StatePublished - Dec 1 1998
EventProceedings of the 1998 32nd Asilomar Conference on Signals, Systems & Computers. Part 1 (of 2) - Pacific Grove, CA, USA
Duration: Nov 1 1998Nov 4 1998

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Vector quantization
Image compression

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

Cite this

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title = "Nearly lossless vector quantization algorithm for compression of remotely sensed images",
abstract = "Most compression algorithms are desired for minimizing a squared error criterion. The squared error criterion does not accurately represent the fidelity requirements for scientific image compression. In this paper we propose a distortion measure which correlates with subjective evaluations, and an adaptive vector quantization algorithm which minimizes this distortion measure. A new approach to codebook design is presented to replace the nearest neighbor approach.",
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