Title :
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
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;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1994. ICASSP-94., 1994 IEEE International Conference on
Conference_Location :
Adelaide, SA
Print_ISBN :
0-7803-1775-0
DOI :
10.1109/ICASSP.1994.389484
