Title :
Hyperspectral image compression using entropy-constrained predictive trellis coded quantization
Author :
Abousleman, Glen P. ; Marcellin, Michael W. ; Hunt, Bobby R.
Author_Institution :
Dept. of Electr. & Comput. Eng., Arizona Univ., Tucson, AZ, USA
fDate :
4/1/1997 12:00:00 AM
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 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
Keywords :
Gaussian processes; Markov processes; autoregressive processes; discrete cosine transforms; entropy codes; image coding; image sequences; prediction theory; source coding; spectral analysis; transform coding; trellis codes; 2D DCT; ECDPCM; MSE performance; PSNR; autoregressive sources; average peak signal-to-noise ratio; eight-state ECPTCQ system; entropy constrained differential pulse code modulation; entropy constrained predictive trellis coded quantization; first-order Gauss-Markov source; hyperspectral image compression; hyperspectral image sequence; iterative optimisation algorithms; mean squared error performance; source coding; spectral bands; training sequence; transform coding; Hyperspectral imaging; Hyperspectral sensors; Image coding; PSNR; Pixel; Pulse modulation; Quantization; Radiometry; Remote sensing; Spectroscopy;
Journal_Title :
Image Processing, IEEE Transactions on
