Image Quality Assessment (IQA) is designed to use computational models to automatically measure image quality in line with the subjective assessment of the human visual system and to apply them to relevant practical problems. Firstly, the reference image and the distorted image are input, and the visual saliency model is used to calculate the feature mapping of the local similarity of the image, which is used as the weighting function in the quality score pooling stage. At the same time, in view of the deficiency of the visual saliency map as a single feature mapping, the gradient amplitude is increased, then the image is transformed into the color space to extract the color features, and finally the corresponding weight is allocated to calculate the image similarity. Results the comparative test on four large data sets shows that while maintaining a moderate computational complexity, vspsi has improved the prediction accuracy compared with other representative models. In particular, the SROCC on the tid2013 data set reaches 0.905 5. The results tell that VSPSI is an IQA with excellent performance. It has good performance in different data sets and different distortion types, and has strong robustness. It can be used to assess the objective quality of multi class distorted images. At the same time, the performance of VSPSI can be further improved by optimizing the visual saliency model.