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Hyperspectral Image Lossless Compression Algorithm Based on Bidirectional Interband Prediction

WANG Lang,GUO Shu-xu(College of Electronic Science and Engineering,Jilin University,Changchun 130012,China)  
To improve the real-time performance of the current compression algorithms on hyperspectral image,a new lossless compression method based on prediction tree with error variances compensated for hyperspectral image is proposed.The method incorporates prediction tree and adaptive interband prediction techniques.The bidirectional interband prediction to current band is applied to hyperspectral image compression.The error created by prediction tree is compensated by linear adaptive predictor which de-correlates spectral statistic redundancy.In consideration of the complexity for the coefficients' calculation,a correlation-driven adaptive estimator is designed with which parameters are uniquely determined by the previously coded pixels.After de-correlating intraband and interband redundancy,an efficient wavelet coding method,SPIHT(Set Partitioning in Hierarchical Trees),is used to encode residual image.The experiments show that the proposed method achieves both low overhead and high compression ratio in comparison with the popular lossless compression algorithm.
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