Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162755
Title: Single image super-resolution quality assessment: a real-world dataset, subjective studies, and an objective metric
Authors: Jiang, Qiuping
Liu, Zhentao
Gu, Ke
Shao, Feng
Zhang, Xinfeng
Liu, Hantao
Lin, Weisi
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Source: Jiang, Q., Liu, Z., Gu, K., Shao, F., Zhang, X., Liu, H. & Lin, W. (2022). Single image super-resolution quality assessment: a real-world dataset, subjective studies, and an objective metric. IEEE Transactions On Image Processing, 31, 2279-2294. https://dx.doi.org/10.1109/TIP.2022.3154588
Journal: IEEE Transactions on Image Processing 
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.
URI: https://hdl.handle.net/10356/162755
ISSN: 1057-7149
DOI: 10.1109/TIP.2022.3154588
Rights: © 2022 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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