Lossless Compression of Multispectral Imagery by Error Compensated Prediction Tree
基于误差补偿预测树的多光谱遥感图像无损压缩方法

A novel error compensated prediction tree method used for lossless compression of multispectral imagery is proposed in this paper. This method combines prediction tree and adaptive linear prediction techniques, so the spatial redundancy, spectral statistical redundancy and spectral structural redundancy in multispectral imagery are all exploited. For each pixel of the imagery, the prediction tree based on interband structural similarity is used to remove the spatial redundancy and a prediction error is created. Then this error is compensated by an interband linear adaptive predictor, which is constructed according to the spectral statistical redundancy. Our method removes the spectral structural and statistic redundancies as well as the spatial redundancy, so the better compression results can be obtained. The construction of adaptive predictor for each pixel will introduce prohibitive computational complexity to the algorithm. Therefore a modified algorithm based on local stability of imagery is also designed to reduce the computational complexity. The experimental results from practical multispectral images have shown that our method is better than the original prediction tree one.