No-Reference Quality Metric of Contrast-Distorted Images Based on Information Maximization
提出一种无参考图像质量评价方法，通过局部未预测区域的信息熵和全局直方图与均匀分布的KL散度，评估对比度失真图像质量，在五个数据库上优于现有方法。
The general purpose of seeing a picture is to attain information as much as possible. With it, we in this paper devise a new no-reference/blind metric for image quality assessment (IQA) of contrast distortion. For local details, we first roughly remove predicted regions in an image since unpredicted remains are of much information. We then compute entropy of particular unpredicted areas of maximum information via visual saliency. From global perspective, we compare the image histogram with the uniformly distributed histogram of maximum information via the symmetric Kullback-Leibler divergence. The proposed blind IQA method generates an overall quality estimation of a contrast-distorted image by properly combining local and global considerations. Thorough experiments on five databases/subsets demonstrate the superiority of our training-free blind technique over state-of-the-art full- and no-reference IQA methods. Furthermore, the proposed model is also applied to amend the performance of general-purpose blind quality metrics to a sizable margin.