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Hyperspectral image compression using entropy-constrained predictive trellis coded quantization

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3 Author(s)
Abousleman, G.P. ; Dept. of Electr. & Comput. Eng., Arizona Univ., Tucson, AZ, USA ; Marcellin, M.W. ; Hunt, B.R.

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

Published in:

Image Processing, IEEE Transactions on  (Volume:6 ,  Issue: 4 )

Date of Publication:

Apr 1997

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