Entropy-constrained predictive trellis coded quantization and compression of hyperspectral imagery.

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, three systems--an ECPTCQ system, a 3-D Discrete Cosine Transform (DCT) system and a hybrid system--are presented for compression of hyperspectral imagery which utilize trellis coded quantization (TCQ). Specifically, the first system utilizes a 2-D DCT and ECPTCQ. The 2-D DCT is used to transform all nonoverlapping 8 x 8 blocks of each band. Thereafter, ECPTCQ is used to encode the transform coefficients in the spectral dimension. The 3-D DCT system uses TCQ to encode transform coefficients resulting from the application of an 8 x 8 x 8 DCT. The hybrid system uses DPCM to spectrally decorrelate the data, while a 2-D DCT coding scheme is used for spatial decorrelation. Side information and rate allocation strategies for all systems are discussed. Entropy-constrained codebooks are optimized for various generalized Gaussian distributions using a modified version of the generalized Lloyd algorithm. The first system can compress a hyperspectral image sequence at 0.125 bits/pixel/band while retaining an average peak signal-to-noise ratio of greater than 43 dB over the spectral bands. The 3-D DCT and hybrid systems achieve compression ratios of 77:1 and 69:1 while maintaining average peak signal-to-noise ratios of 40.75 dB and 40.29 dB, respectively, over the coded bands.