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ASSP: An adaptive sample statistics-based pooling for full-reference image quality assessment

Ling, Y; Zhou, F; Guo, K; Xue, J-H; (2022) ASSP: An adaptive sample statistics-based pooling for full-reference image quality assessment. Neurocomputing 10.1016/j.neucom.2021.12.098. (In press).

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Abstract

Most full-reference image quality assessment (IQA) models first compute local quality scores and then pool them into an overall score. In this paper, we develop an innovative pooling strategy based on sample statistics to adaptively make the IQA more consistent with human visual assessment. The innovation of this work is threefold. First, we identify that standard sample statistics and robust sample statistics could provide complementary information about the degree of degradation in distorted images. Second, an effective IQA metric is proposed by adaptively integrating robust sample statistics and standard sample statistics via excess kurtosis. Third, instead of using the statistics directly, we adjust them by taking into account the global change of image gradients to avoid exaggerating the degradation degree. Experiments conducted on five well-known IQA databases demonstrate the effectiveness of the proposed pooling strategy in terms of high prediction accuracy and monotonicity.

Type: Article
Title: ASSP: An adaptive sample statistics-based pooling for full-reference image quality assessment
DOI: 10.1016/j.neucom.2021.12.098
Publisher version: https://doi.org/10.1016/j.neucom.2021.12.098
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: IQA, statistics-based pooling, robust sample statistics, excess kurtosis
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10141289
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