基于三维自适应预测的高光谱图像无损压缩算法

利用高光谱图像具有较强的谱间相关性的特点,本文提出一种基于三维自适应预测的高光谱图像无损压缩方法,首先根据相关系数计算波段预测顺序,然后利用相关性较强的空间邻点和谱间邻点,采用基于神经网络模型的自适应预测方法进行三维预测编码.实验结果表明,该方法能够有效的去除高光谱图像的空间和谱间相关性,与现在最优的无损压缩国际标准JPEG-LS相比,压缩后的平均比特率能够降低0.3bpp左右.

Lossless Compression of Hyperspectral Image Based on 3-D Adaptive Prediction

Using the significant spectral correlation within the hyperspectral images,a lossless compression algorithm is presented in this paper.The method can be explained in two steps:(1)band ordering according to spectral correlation coefficient;(2)3-D adaptive prediction based on neural networks.Experiments show that this method can remove the spatial and spectral redundancy efficiently.The proposed approach can reduce about 0.3 bit per pixel compression compared with JPEG-LS.