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 parameters.The quantified information fidelity is regarded as an efficient image quality assessment criterion.The proposed statistical measure 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.