The work aims to develop an image quality assessment (IQA) model with high comprehensive benefits in terms of accuracy, generalization and complexity. Combined with image distortion characteristics and human spatial visual system (HVS) characteristics and based on the structural similarity index (SSIM) model, an IQA method and model based on the image structure perception similarity (ICBS) was proposed. In the method, firstly, a mathematical model for human detecting the real images was proposed combined with the characteristics of the contrast sensitivity, frequency sensitivity, luminance nonlinearity and masking of HVS. Then, the perception of human eyes to the real image was simulated and the real images were processed with the proposed image perception model to reduce visual redundancy. Thus, the perceptual images were obtained. Finally, based on the modeling method of SSIM and combined with the characteristics of perceptual image, an IQA model (namely ICBS) was proposed. Further, in order to illustrate the performance of ICBS, 4 645 distorted images of 35 distortion types in three databases were used for simulation from two aspects of overall evaluation and IQA for each distortion type. The results showed that the accuracy of ICBS was achieved in three databases, with a weighted PLCC of 0.917 and SROCC of 0.912. According to the comprehensive benefits of accuracy, generalization and complexity, the IQA results were compared with those of 7 existing IQA models. The results showed that the accuracy of ICBS was 10.77% higher than those of SSIM in three databases, averagely, and its performance was better than that of 7 existing IQA models. Combined with the experimental results and theoretical analysis, it is showed that ICBS is an effective and excellent IQA model.