title: Lossless compression of hyperspectral images using hybrid context prediction.
abstract: 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.
authors.affiliation: School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China. tacal@163.com
authors.forename: Yuan
authors.initials: Y
authors.lastname: Liang
authors.forename: Jianping
authors.initials: J
authors.lastname: Li
authors.forename: Ke
authors.initials: K
authors.lastname: Guo
mesh_terms: D000465:Algorithms; D013057:Spectrum Analysis / Q000379:methods*