超光谱图像的二阶差分预测压缩算法

New Compression Approach to Hyper-spectral Images Based on Second Order Difference Predictive

根据超光谱图像空间谱间都存在较强相关性的特性,设计了一种结合空间预测的二阶差分预测压缩算法。采用MED预测器去除空间相关,采用二阶差分预测器去除谱间相关,并根据像素预测误差的权重设计了统一的去相关预测器,最后对误差图像做基于上下文的编码,实现图像的近无损压缩。研究结果表明,各波段峰值信噪比(PSNR)为39dB左右时,压缩比可以达到12.7,压缩效果比较理想。

According to hyper-spectral images having strong correlation both in spectral and spatial, a novel compression scheme based on second order difference predictive that combines with spatial predictive was presented. MED predictorwas used to remove the spatial correlation. Second order difference predictor was used to remove spectral correlation.Then a unified predictor was designed based on the weight of predictive error. At last near lossless compression was completed after context based coding. The results show that the compression ratio can reach up w 12. 7 when the PSNR is about 39413, so the algorithm is efficient.