Multi Pattern Prediction Based on Lossless Compression Of Multispectral Image Data
基于多方式预测的多波段遥感图象无损压缩

According to the spatial and spectral structural characteristics of multi spectral image data, this paper present a concept of Multi Pattern Prediction : given a principle, any pixel in an image can be predicted by any prediction function selected from an alternative function set in order to decorrelate the image more efficiently ; at the same time, taking the advantage of spectral structural correlation, the spectral adjacent pixels are decorrelated by the same prediction function, so the additional cost of storage in Multi Pattern Prediction would be reduced smartly. We present the Minimum Entropy Principle as the theoretic principle to select the predition function. And we get an equivalent principle named as Maximal Frequency of Minimum-Error Standard. Experiments on TM images show that this method can decorrelate images much more efficiently and lead to higher compression ratios.