基于熵约束预测网格编码量化的高光谱图像压缩。

Hyperspectral image compression using entropy-constrained predictive trellis coded quantization.

机构信息

Dept. of Electr. and Comput. Eng., Arizona Univ., Tucson, AZ.

出版信息

IEEE Trans Image Process. 1997;6(4):566-73. doi: 10.1109/83.563321.

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.

摘要

提出了一种基于训练序列的熵约束预测网格编码量化（ECPTCQ）方案，用于对自回归源进行编码。对于一阶高斯-马尔可夫源的编码，八状态 ECPTCQ 系统的均方误差（MSE）性能优于熵约束差分脉冲编码调制（ECDPCM），最高可达 1.0 dB。此外，还开发了一种利用 ECPTCQ 的高光谱图像压缩系统。在 0.125 b/pixel/band 处压缩的高光谱图像序列在各个光谱带的平均峰值信噪比（PSNR）大于 43 dB。