Contrast Changed Image Quality Assessment Based on Dual-Path Feature-Difference Network
Dixiu Zhong, Ping An Shi, Da Pan, Yuan Jin sha, Zefeng Ying, Xiaojie Bao, Ming Hou · 2017
Contrast plays an important role in human visual perception while it is usually unsatisfactory due to various factors in the acquisition process of images. With numerous approaches proposed to enhance contrast, less work has been dedicated to contrast changed image quality assessment (IQA). In this work, we propose a quality assessment model based on dual-path feature-difference, which uses a dual-channel convolution neural network (CNN) to extract features, removing the common feature of reference image and distorted image and preserving the feature-difference caused by contrast change. Then, image quality scores are estimated based on feature-difference maps. Moreover, a patch contrast quality map can also be created during the score prediction process, which is useful for the optimization of the image contrast enhancement algorithms. Validations based on multiple publicly available datasets show that the proposed method is well correlated with subjective evaluations of contrast changed image quality.