k-NN Based Bypass Entropy and Mutual Information Estimation for Incremental Remote-Sensing Image Compressibility Evaluation

• Published in: China communications 2017-08, Vol.14 (8), p.54-62
• Main Authors: Liu, Xijia, Tao, Xiaoming, Duan, Yiping, Ge, Ning
• Format: Article
• Language: English
• Subjects: Correlation; Distribution functions; Entropy; Estimation; Image coding; incremental image compression; K近邻; Mutual information; Remote sensing; 互信息; 信息评估; 图像压缩技术; 增量; 旁路; 熵估计; 遥感图像压缩
• ISSN: 1673-5447
• DOI: 10.1109/CC.2017.8014347

Incremental image compression techniques using priori information are of significance to deal with the explosively increasing remote-sensing image data. However, the potential benefi ts of priori information are still to be evaluated quantitatively for effi cient 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.