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

Author

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

Author_Institution

Dept. of Electr. & Comput. Eng., Arizona Univ., Tucson, AZ, USA

Volume

v

fYear

1994

fDate

19-22 Apr 1994

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 bits/pixel/band retains peak signal-to-noise ratios greater than 42 dB over most spectral bands

Keywords

Markov processes; autoregressive processes; data compression; entropy codes; image coding; image sequences; prediction theory; source coding; spectral analysis; trellis codes; ECPTCQ; autoregressive sources; encoding; entropy-constrained DPCM; entropy-constrained quantization; first-order Gauss-Markov source; hyperspectral image compression; hyperspectral image sequence; predictive trellis coded quantization; signal-to-noise ratios; spectral bands; training sequence; Application software; Entropy; Gaussian processes; Hyperspectral imaging; Hyperspectral sensors; Image coding; PSNR; Quantization; Remote sensing; Spectroscopy;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1994. ICASSP-94., 1994 IEEE International Conference on

Conference_Location

Adelaide, SA

ISSN

1520-6149

Print_ISBN

0-7803-1775-0

Type

conf

DOI

10.1109/ICASSP.1994.389484

Filename

389484