Blind Image Quality Assessment Based on Rank-Order Regularized Regression
Blind image quality assessment (BIQA) aims to estimate the subjective quality of a query image without access to the reference image. Existing learning-based methods typically train a regression function by minimizing the average error between subjective opinion scores and model predictions. However, minimizing average error does not necessarily lead to correct quality rank-orders between the test images, which is a highly desirable property of image quality models. In this paper, we propose a novel rank-order regularized regression model to address this problem. The key idea is to introduce a pairwise rank-order constraint into the maximum margin regression framework, aiming to better preserve the correct perceptual preference. To the best of our knowledge, this is the first attempt to incorporate rank-order constraints into margin-based quality regression model. By combing with a new local spatial structure feature, we achieve highly consistent quality prediction with human perception. Experimental results show that the proposed method outperforms many state-of-the-art BIQA metrics on popular publicly available IQA databases (i.e., LIVE-II, TID2013, VCL@FER, LIVEMD, and ChallengeDB).