中文摘要：

本文提出了一个图像质量盲评估的统计测度.该测度首先根据自然图像的统计性质与失真图像的模型,实现对图像小波系数分布参数的盲估计;再利用估计的分布参数来计算失真图像与参考图像之间的互信息,以量化失真图像对参考图像的保真度,进而实现对图像质量的评估.本文提出的测度避免了对参考图像的依赖,且克服了现有图像质量盲评估对特征选择与提取、机器学习等过程的依赖.LIVE图像质量评估数据库的总体评估结果表明：本文提出的盲评估统计测度对图像质量评估结果与数据库的主观评估结果高度一致,且优于文献中报道的盲评估测度.

英文摘要：

A statistical measure for blind image quality assessment （IQA） is proposed. The wavelet coefficients＇ distribution parameters of the distorted images are blind estimated based on the natural scene statistics and the image distortion model;the mutual information between the distorted and the corresponding reference images is further calculated from the estimated distribution param- eters. The quantified information fidelity is regarded as an efficient image quality assessment criterion. The proposed statistical mea- sure in this paper does not require any prior information of the reference image and avoids the feature selection, feature extraction and machine learning processes required by existing blind image quality assessment methods. Evaluated on the LIVE IQA database, it is demonstrated that the proposed statistical measure corresponds well with the subjective human evaluations and outperforms the state-of-art blind IQA algorithms.