An information based framework for performance evaluation of image enhancement methods

We propose a new framework for evaluating image enhancement methods. In this work, we focus on contrast enhancement, and use the joint probability and the mutual information derived from the image co-occurrence matrix to determine the proposed index. We show that the second order entropy and the mutual information can be used jointly for tracking and evaluating the effect of contrast enhancement. More importantly, we show that the variations of the mutual information mimic, in a consistent way, human perceptual sensitivity to visual appearance of image changes due to contrast enhancement. The proposed approach is seen as a completely new framework for evaluating image enhancement methods, validated by a number of experiments. Finally, we also show that the proposed metric can even be used to detect image enhancement thresholds at which unpleasant artifacts start to be perceived by viewers.