Hyperspectral image compression using entropy-constrained predictive trellis coded quantization

Glen P. Abousleman, Michael W. Marcellin, Bobby R. Hunt

Research output: Contribution to journalArticle

50 Scopus citations

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 mean squared error (MSE) performance of an eight-state ECPTCQ system exceeds that of entropy-constrained differential pulse code modulation (ECDPCM) by up to 1.0 dB. In addition, a hyperspectral image compression system is developed, which utilizes ECPTCQ. A hyperspectral image sequence compressed at 0.125 b/pixel/band retains an average peak signal-to-noise ratio (PSNR) of greater than 43 dB over the spectral bands.

Original languageEnglish (US)
Pages (from-to)566-573
Number of pages8
JournalIEEE Transactions on Image Processing
Volume6
Issue number4
DOIs
StatePublished - Dec 1 1997

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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