Hyperspectral image compression using entropy-constrained predictive trellis coded quantization.
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.
Abousleman
G P
GP
Dept. of Electr. and Comput. Eng., Arizona Univ., Tucson, AZ.
Marcellin
M W
MW
Hunt
B R
BR