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...

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Bibliographic Details
Published in:IEEE transactions on multimedia 2017-11, Vol.19 (11), p.2490-2504
Main Authors: Qingbo Wu, Hongliang Li, Zhou Wang, Fanman Meng, Bing Luo, Wei Li, Ngan, King N.
Format: Article
Language:English
Subjects:
Constraint modelling
Distortion
Error correction
Image quality
Image quality assessment
Learning systems
Measurement
Optimization
Predictive models
Quality
Quality assessment
rank-order regularized regression
Regression models
Training
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Summary: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).
ISSN:1520-9210
1941-0077
DOI:10.1109/TMM.2017.2700206