As research hotspot in recent years

no-reference (NR)image quality assessment has profound practical significance and broad application value.We present a new method of no-reference image quality assessment (IQA) based on mutual information(MIQA). Original natural images and their corresponding normalized luminance field and local standard deviation field are used as inputs.Self-correlated mutual information is used to quantify the correlations between neighboring pixels of three categories of inputs

and the quantization results are used as features.In addition

the multiscale analysis is introduced to obtain the mutual information features across two scales.The image distortion classifier and quality prediction model are trained by using a support vector machine (SVM) on the LIVE image database and conduct the NR IQA across multiple categories of distortions. We conduct the performance evaluation for our proposed algorithm on the LIVE image database

the experimental results show that the mean correlation coefficient between the quality judgment of this algorithm and the human subjective quality judgment is up to 0.93

and the total classification accuracy is up to 79%

delivering a performance which is competitive with the most popular full-reference (FR)/NR IQA methods. The method presented is different from the traditional NR IQA methods based on image transforms.Since natural images are highly structured

we focus on the inherent correlations between neighboring pixels of natural images

rather than the distribution of transformed coefficients

and obtain a good performance.Since the method presented is build without any image transforms and it is a global method

it has a relatively low time complexity.