No Reference Image Quality Assessment Via Quality Difference Learning
Jiaming Xie, Yu Luo, Jie Ling, Guanghui Yue · 2023
For human beings, there is a natural preference for judging the relative quality rather than directly predicting the quality score of an image. Based on this view, we propose an image quality difference learning network (IQDLNet) for evaluating image quality in a no-reference manner. Specifically, the proposed IQDLNet consists of a quality difference-aware network (QDAN) and a quality assessment network (QAN). The QDAN aims to predict the score difference between two randomly matched images and the QAN aims to predict the quality score of these two images. To further enhance the mutual understanding of image semantics, a semantic interaction module (SIM) is proposed with a dual regressor set up to carry out competitive learning in combination with the quality difference-aware feature. Experimental results on five IQA datasets demonstrate the superior performance of the proposed method over eight state-of-the-arts.