Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.neucom.2018.11.015
Title: Blind image quality assessment based on joint log-contrast statistics
Authors: Li, Qiaohong 
Lin, Weisi 
Gu, Ke
Zhang, Yabin
Fang, Yuming
Keywords: Science & Technology
Technology
Computer Science, Artificial Intelligence
Computer Science
Blind image quality assessment (BIQA)
No-reference (NR)
Natural scene statistics
Partial least square
NATURAL SCENE STATISTICS
PARTIAL LEAST-SQUARES
FRAMEWORK
Issue Date: 28-Feb-2019
Publisher: ELSEVIER SCIENCE BV
Citation: Li, Qiaohong, Lin, Weisi, Gu, Ke, Zhang, Yabin, Fang, Yuming (2019-02-28). Blind image quality assessment based on joint log-contrast statistics. NEUROCOMPUTING 331 : 189-198. ScholarBank@NUS Repository. https://doi.org/10.1016/j.neucom.2018.11.015
Abstract: © 2018 During recent years, quality-aware features extracted from natural scene statistics (NSS) models have been used in development of blind image quality assessment (BIQA) algorithms. Generally, the univariate distributions of bandpass coefficients are used to fit a parametric probabilistic model and the model parameters serve as the quality-aware features. However, the inter-location, inter-direction and inter-scale correlations of natural images cannot be well exploited by such NSS models, as it is hard to capture such dependencies using univariate marginal distributions. In this paper, we build a novel NSS model of joint log-contrast distribution to take into account the across space and direction correlations of natural images (inter-scale correlation to be explored as the next step). Furthermore, we provide a new efficient approach to extract quality-aware features as the gradient of log-likelihood on the NSS model, instead of using model parameters directly. Finally, we develop an effective joint-NSS model based BIQA metric called BJLC (BIQA based on joint log-contrast statistics). Extensive experiments on four public large-scale image databases have validated that objective quality scores predicted by the proposed BIQA method are in higher accordance with subjective ratings generated by human observers compared with existing methods.
Source Title: NEUROCOMPUTING
URI: https://scholarbank.nus.edu.sg/handle/10635/155392
ISSN: 0925-2312
1872-8286
DOI: 10.1016/j.neucom.2018.11.015
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