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Single image super-resolution quality assessment: a real-world dataset, subjective studies, and an objective metric

Jiang, Qiuping, Liu, Zhentao ORCID: https://orcid.org/0000-0003-4544-3481, Gu, Ke, Shao, Feng, Zhang, Xinfeng, Liu, Hantao ORCID: https://orcid.org/0000-0003-4544-3481 and Weisi, Lin 2022. Single image super-resolution quality assessment: a real-world dataset, subjective studies, and an objective metric. IEEE Transactions on Image Processing 31 , pp. 2279-2294. 10.1109/TIP.2022.3154588

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Abstract

Numerous single image super-resolution (SISR) algorithms have been proposed during the past years to reconstruct a high-resolution (HR) image from its low-resolution (LR) observation. However, how to fairly compare the performance of different SISR algorithms/results remains a challenging problem. So far, the lack of comprehensive human subjective study on large-scale real-world SISR datasets and accurate objective SISR quality assessment metrics makes it unreliable to truly understand the performance of different SISR algorithms. We in this paper make efforts to tackle these two issues. Firstly, we construct a real-world SISR quality dataset (i.e., RealSRQ ) and conduct human subjective studies to compare the performance of the representative SISR algorithms. Secondly, we propose a new objective metric, i.e., KLTSRQA , based on the Karhunen-Loéve Transform (KLT) to evaluate the quality of SISR images in a no-reference (NR) manner. Experiments on our constructed RealSRQ and the latest synthetic SISR quality dataset (i.e., QADS ) have demonstrated the superiority of our proposed KLTSRQA metric, achieving higher consistency with human subjective scores than relevant existing NR image quality assessment (NR-IQA) metrics. The dataset and the code will be made available at https://github.com/Zhentao-Liu/RealSRQ-KLTSRQA .

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: Institute of Electrical and Electronics Engineers
ISSN: 1057-7149
Date of First Compliant Deposit: 28 February 2022
Date of Acceptance: 18 February 2022
Last Modified: 26 Nov 2022 14:42
URI: https://orca.cardiff.ac.uk/id/eprint/147856

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