%0 Journal Article
%A Xijia Liu
%A Xiaoming Tao
%A Yiping Duan
%A Ning Ge
%T k-NN Based Bypass Entropy and Mutual Information Estimation for Incremental Remote-Sensing Image Compressibility Evaluation
%D 2017
%R
%J China Communications
%P 54-62
%V 14
%N 8
%X Incremental image compression techniques using priori information are of significance to deal with the explosively increasing remote-sensing image data. However, the potential benefits of priori information are still to be evaluated quantitatively for efficient compression scheme designing. In this paper, we present a k-nearest neighbor (k-NN) based bypass image entropy estimation scheme, together with the corresponding mutual information estimation method. Firstly, we apply the k-NN entropy estimation theory to split image blocks, describing block-wise intra-frame spatial correlation while avoiding the curse of dimensionality. Secondly, we propose the corresponding mutual information estimator based on feature-based image calibration and straight-forward correlation enhancement. The estimator is designed to evaluate the compression performance gain of using priori information. Numerical results on natural and remote-sensing images show that the proposed scheme obtains an estimation accuracy gain by 10% compared with conventional image entropy estimators. Furthermore, experimental results demonstrate both the effectiveness of the proposed mutual information evaluation scheme, and the quantitative incremental compressibility by using the priori remote-sensing frames.
%U http://www.cic-chinacommunications.cn/EN/abstract/article_555.shtml