A metric for quantifying image quality induced saliency variation

Guo, Pengfei, Zhao, Xin, Zeng, Delu and Liu, Hantao ORCID: https://orcid.org/0000-0003-4544-3481 2021. A metric for quantifying image quality induced saliency variation. Presented at: 2021 IEEE International Conference on Image Processing (ICIP), Anchorage, AK, USA, 19-22 September 2021. Proceedings of the IEEE International Conference on Image Processing. IEEE, pp. 1459-1463. 10.1109/ICIP42928.2021.9506154

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Abstract

Saliency plays an important role in the area of image quality assessment. Image distortions cause shift/redistribution of saliency from its original places. There is a need to be able to measure such distortion-included saliency variation (DSV), so that the use of saliency can be optimised for automated image quality assessment. Effort has been made in our previous study to build a benchmark for the measurement of DSV through subjective testing. In this paper, we demonstrate that exiting similarity measures are unhelpful for the quantification of DSV. Thus, we propose a new metric for DSV combining local and global measures using convex optimization. The experimental results show that our proposed metric can accurately quantify saliency variation.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Schools > Computer Science & Informatics
Publisher:	IEEE
ISBN:	978-1-6654-3102-6
ISSN:	1522-4880
Last Modified:	10 Jul 2025 14:42
URI:	https://orca.cardiff.ac.uk/id/eprint/142442

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