Entropy-constrained predictive trellis coded quantization: Application to hyperspectral image compression

G. P. Abousleman, M. W. Marcellin, B. R. Hunt

Research output: Contribution to journalArticle

Abstract

A training-sequence-based entropy-constrained predictive trellis coded quantization (ECPTCQ) scheme is presented for encoding autoregressive sources. For encoding a first-order Gauss-Markov source, the MSE performance of an 8-state ECPTCQ system exceeds that of entropy-constrained DPCM by up to 1.0 dB. In addition, a hyperspectral image compression system is developed which utilizes ECPTCQ. A hyperspectral image sequence compressed at 0.15 bitspixelband retains peak signal-to-noise ratios greater than 42 dB over most spectral bands.

Original languageEnglish (US)
Article number389484
Pages (from-to)V253-V256
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume5
StatePublished - Jan 1 1994

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ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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