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Convex optimization method for quantifying image quality induced saliency variation

Guo, Pengfei, Zhao, Xin, Wu, Dongqing, Zhang, Lei, Zeng, Delu and Liu, Hantao ORCID: 2021. Convex optimization method for quantifying image quality induced saliency variation. IEEE Access 9 , pp. 111533-111543. 10.1109/ACCESS.2021.3102465

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Visual saliency plays a significant role in image quality assessment. Image distortions cause shift of saliency from its original places. Being able to measure such distortion-included saliency variation (DSV) contributes towards the optimal use of saliency in automated image quality assessment. In our previous study a benchmark for the measurement of DSV through subjective testing was built. However, exiting saliency similarity measures are unhelpful for the quantification of DSV due to the fact that DSV highly depends on the dispersion degree of a saliency map. In this paper, we propose a novel similarity metric for the measurement of DSV, namely MDSV, based on convex optimization method. The proposed MDSV metric integrates the local saliency similarity measure and the global saliency similarity measure using the function of saliency dispersion as a modulator. We detail the parameter selection of the proposed metric and the interactions of sub-models for the convex optimization strategy. Statistical analyses show that our proposed MDSV outperforms the existing metrics in quantifying the image quality induced saliency variation.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: Institute of Electrical and Electronics Engineers
ISSN: 2169-3536
Date of First Compliant Deposit: 2 August 2021
Date of Acceptance: 24 July 2021
Last Modified: 15 Nov 2023 20:23

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