Image quality assessment (IQA) plays a very important role in acquiring, storing, transmitting and processing image and video. Using the characteristics of human visual perception and the features of the gray, gradient, local contrast, and blurring of image, an IQA method based on the image content contrast perception is proposed in the paper, which is called MPCC. In the proposed method, firstly, combining with the characteristics of human visual perception, based on the definition of the contrast in physics, a novel definition for image quality and its calculation method are proposed. Then, based on the gray gradient co-occurrence matrix, a novel concept, namely the gray-gradient entropy of image, and its calculation method, are proposed. And based on the gray-gradient entropy, local contrast and blurring of image, a method of describing the image content and their visual perception are proposed. Finally, based on the image content features and the image quality definition, an IQA method and its mathematical model are proposed by comprehensive analysis. Further, the proposed IQA model MPCC is tested by using 119 reference images and 6395 distorted images from the five open image databases (LIVE, CSIQ, TID2008, TID2013 and IVC). Moreover, the influences of the 52 distortion types on IQA are analyzed. In addition, in order to illustrate the advantages of the MPCC model, it is compared with the seven existing typical IQA models in terms of the accuracy, complexity and generalization performance of model. The experimental results show that the accuracy PLCC of the MPCC model can achieve 0.8616 at lowest and 0.9622 at most in the five databases; among the 52 distortion types, the two distortion types, namely the change of color saturation and the local block-wise distortions of different intensity, have the greatest influence on IQA, and the accuracy PLCC values of the seven existing IQA models are almost all below 0.6, but the PLCC of the MPCC model can reach more than 0.68; and the comprehensive benefit of the performance of the MPCC model is better than those of the seven existing IQA models. These results of test and comparison above show that the proposed IQA method is effective and feasible, and the corresponding model has an excellent performance.