Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/151329
Title: Blind image quality assessment based on joint log-contrast statistics
Authors: Li, Qiaohong
Lin, Weisi
Gu, Ke
Zhang, Yabin
Fang, Yuming
Keywords: Engineering::Computer science and engineering
Issue Date: 2019
Source: Li, Q., Lin, W., Gu, K., Zhang, Y. & Fang, Y. (2019). Blind image quality assessment based on joint log-contrast statistics. Neurocomputing, 331, 189-198. https://dx.doi.org/10.1016/j.neucom.2018.11.015
Journal: Neurocomputing
Abstract: 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.
URI: https://hdl.handle.net/10356/151329
ISSN: 0925-2312
DOI: 10.1016/j.neucom.2018.11.015
Rights: © 2018 Elsevier B.V. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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