Liang Yuan, Li Jianping, Guo Ke
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China.
Opt Express. 2012 Mar 26;20(7):8199-206. doi: 10.1364/OE.20.008199.
In this letter a new algorithm for lossless compression of hyperspectral images using hybrid context prediction is proposed. Lossless compression algorithms are typically divided into two stages, a decorrelation stage and a coding stage. The decorrelation stage supports both intraband and interband predictions. The intraband (spatial) prediction uses the median prediction model, since the median predictor is fast and efficient. The interband prediction uses hybrid context prediction. The hybrid context prediction is the combination of a linear prediction (LP) and a context prediction. Finally, the residual image of hybrid context prediction is coded by the arithmetic coding. We compare the proposed lossless compression algorithm with some of the existing algorithms for hyperspectral images such as 3D-CALIC, M-CALIC, LUT, LAIS-LUT, LUT-NN, DPCM (C-DPCM), JPEG-LS. The performance of the proposed lossless compression algorithm is evaluated. Simulation results show that our algorithm achieves high compression ratios with low complexity and computational cost.
在这封信中，提出了一种使用混合上下文预测的高光谱图像无损压缩新算法。无损压缩算法通常分为两个阶段，去相关阶段和编码阶段。去相关阶段支持带内和带间预测。带内（空间）预测使用中值预测模型，因为中值预测器快速且高效。带间预测使用混合上下文预测。混合上下文预测是线性预测（LP）和上下文预测的组合。最后，混合上下文预测的残差图像通过算术编码进行编码。我们将提出的无损压缩算法与一些现有的高光谱图像算法进行比较，如3D-CALIC、M-CALIC、LUT、LAIS-LUT,、LUT-NN、DPCM（C-DPCM）、JPEG-LS。对提出的无损压缩算法的性能进行了评估。仿真结果表明，我们的算法以低复杂度和计算成本实现了高压缩比。
