No-reference Quality Assessment for Contrast-distorted Images Based on Gray and Color-gray-difference Space

Abstract

No-reference image quality assessment is a basic and challenging problem in the field of image processing. Among them, contrast distortion has a great impact on the perception of image quality. However, there are relatively few studies on no-reference quality assessment of contrast-distorted images. This article proposes a no-reference quality assessment algorithm for contrast-distorted images based on gray and color-gray-difference (CGD) space. In terms of gray space, we consider the local and global aspects, and use the distribution characteristics of the grayscale histogram to represent global features, while local features are described by the fusion of Local Binary Pattern (LBP) operator and gradient. In terms of CGD space, we first randomly extract patches from the entire image and then extract appropriate quality perception features in the patch’s CGD histogram. Finally, the AdaBoosting back propagation (BP) neural network is used to train the prediction model to predict the quality of the contrast-distorted image. Extensive analysis and cross-validation are carried out on five contrast-related image databases, and the experimental results have proved the superiority of this method compared with recent related algorithms.