It aimed to propose an image quality assessment （IQA） method that conformed to visual perception and had high comprehensive benefits of accuracy， generalization， and complexity， which would better control image processing and meet its practical application needs. Based on the image tableau information and their features such as brightness， chromaticity， texture， clarity， and local contrast， and considering human perception effects such as contrast sensitivity， non-linear perception of brightness， visual comfort， and texture complexity perception， a no-reference IQA method， namely BCTCSP， was proposed. In BCTCSP， firstly， by analyzing the relationship between image quality and image brightness， grayscale distribution， color depth and saturation， non-linear perception of brightness， and visual perception comfort， a quantitative and computational method was proposed to obtain the contribution and impact of image brightness， chromaticity， and visual perception on IQA. Then， combining the gray-gradient co-occurrence matrix to calculate and statistically analyze image texture features， and using texture weighted averaging and the HVS complex object perception model， a method was proposed to quantify and calculate the contribution and impact of image texture information entropy and its visual perception on IQA. Next， the contrast value and detection threshold of each point in the image were calculated， subsequently combining the contrast sensitivity characteristics and their models， and masking properties of HVS， the contribution and impact of local contrast and visual perception of the image on IQA were quantified and calculated. Afterwards， four factors including sharpness， signal-to-noise ratio， proportion of high-frequency components， and resolution， were used to describe the clarity of the image， and their quantification and calculation methods were proposed to obtain the clarity index of the image. Finally， synthesizing four factors， an IQA model was constructed， and its measurement standards were quantified. Meanwhile， 6 430 distorted images from 6 open databases （TID2013， CSIQ， LIVE， IVC， SPAQ， and Koniq-10k） were tested and verified， and in terms of accuracy， complexity， generalization， and their comprehensive benefits， BCTCSP was compared with 28 existing and typical IQA models. The experimental results show that the accuracy PLCC of the proposed model reaches a minimum of 0.892 1， a maximum of 0.966 4 among the 6 databases， and the weighted PLCC of 6 databases reaches 0.917 4. Its comprehensive benefits are higher than those of the 28 existing IQA models. The comprehensive results indicate that the proposed model is an effective and high-performance NR-IQA model.

image quality assessment;image tableau;visual perception;texture information entropy