No-reference super-resolution image quality assessment method using multi-layer perceptron regression

Abstract

In order to solve the problem of poor consistency between the traditional super-resolution image quality assessment (SRIQA) index and human subjective perception, this paper presents a reference free super-resolution image quality evaluation method by using multi-layer perceptron (MLP). This method used the pre trained VGG16 network to extract the perceptual quality features of the super-resolution image, and established the regression model between the perceptual quality features of the super-resolution image and the corresponding subjective quality score through MLP. Experimental results show that the Pearson linear correlation coefficient (PLCC) and Spearman rank order correlation coefficient (SROCC) of the proposed algorithm on the public super-resolution image data set exceed 0.95, which is significantly superior to other existing image quality evaluation methods, and has higher consistency with human subjective perception.

Keywords

• depth feature
• multi-layer perceptron
• super-resolution image quality assessment
• regression model
• mean opinion score