Image super-resolution quality assessment: structural fidelity versus statistical naturalness

Zhou, Wei, Wang, Zhou and Chen, Zhibo 2021. Image super-resolution quality assessment: structural fidelity versus statistical naturalness. Presented at: 13th International Conference on Quality of Multimedia Experience (QoMEX), Montreal, QC, Canada, 14-17 June 2021. 2021 13th International Conference on Quality of Multimedia Experience (QoMEX). IEEE, pp. 61-64. 10.1109/QoMEX51781.2021.9465479

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Abstract

Single image super-resolution (SISR) algorithms reconstruct high-resolution (HR) images with their low-resolution (LR) counterparts. It is desirable to develop image quality assessment (IQA) methods that can not only evaluate and compare SISR algorithms, but also guide their future development. In this paper, we assess the quality of SISR generated images in a two-dimensional (2D) space of structural fidelity versus statistical naturalness. This allows us to observe the behaviors of different SISR algorithms as a tradeoff in the 2D space. Specifically, SISR methods are traditionally designed to achieve high structural fidelity but often sacrifice statistical naturalness, while recent generative adversarial network (GAN) based algorithms tend to create more natural-looking results but lose significantly on structural fidelity. Furthermore, such a 2D evaluation can be easily fused to a scalar quality prediction. Interestingly, we find that a simple linear combination of a straightforward local structural fidelity and a global statistical naturalness measures produce surprisingly accurate predictions of SISR image quality when tested using public subject-rated SISR image datasets. Code of the proposed SFSN model is publicly available at https://github.con/weizhou-geek/SFSN.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	IEEE
ISBN:	978-1-6654-1183-7
ISSN:	2472-7814
Last Modified:	27 Sep 2023 16:00
URI:	https://orca.cardiff.ac.uk/id/eprint/162063

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