Remote Sensing Image Loss less Compression Based on 3D Context Prediction

doi:10.11834/jig.2001011235

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Remote Sensing Image Loss less Compression Based on 3D Context Prediction
Vol. 6, Issue 11, Pages: 1107(2001)
Published：2001
DOI： 10.11834/jig.2001011235
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DOI：

Remote Sensing Image Loss less Compression Based on 3D Context Prediction[J]. Journal of Image and Graphics, 2001, 6(11): 1107. DOI： 10.11834/jig.2001011235.

摘要

随着遥感技术的发展，其应用领域大大拓展，随之也带来了遥感数据的海量增长，这给存储、传输都带来极大困难，因此必须对遥感数据进行压缩，为了实现遥感数据的高效压缩，提出了一种基于三维上下文预测测的无损压缩编码算法，该算法包括如下三部分；首先，结合多波段遥感图象的特点，综合考虑其二维的空间相关性和谱间相关性，建立了三维预测模型；其次，在三维预测的基础上，进一步进行上下文预测；最后，引入二次预测的思想来去除图象的统计相关性，从而使残差图象的熵得到最大程度的降低，实验结果表明，该算法大大优于最优JPEG；而对AVHRR图象，其预测效果也明显优于空间和谱间最佳线性预测方法（SSOLP）。

Abstract

With the development of remote sensing technology

it has been applied in more and more domains.\nTherefore

it is necessary to efficiently compress remote sensing data because of the rapid increasing of remote sensing data. In this paper the algorithm includes three parts: first

based on the spatial and inter-spectral correlation of multi-spectral remote sensing images

a 3D prediction model is set up

then the context prediction is made; finally

the re-prediction is used to remove the statistic correlation in the images

and it results in the most decreasing of entropy of the residue. The experimental results represent that the put forward algorithm is much more efficient than the Best JPEG

in addition

it is more efficient for AVHRR images than Spatial &Spectral Optimal Linear Prediction (SSOLP).