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Comparison of single image HDR reconstruction methods — the caveats of quality assessment
University of Cambridge, United Kingdom.
University of Cambridge, United Kingdom.
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-9217-9997
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
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2022 (English)In: SIGGRAPH '22: ACM SIGGRAPH 2022 Conference Proceedings / [ed] Munkhtsetseg Nandigjav,Niloy J. Mitra, Aaron Hertzmann, New York, NY, United States: Association for Computing Machinery (ACM), 2022, p. 1-8, article id 1Conference paper, Published paper (Refereed)
Abstract [en]

As the problem of reconstructing high dynamic range (HDR) imagesfrom a single exposure has attracted much research effort, it isessential to provide a robust protocol and clear guidelines on howto evaluate and compare new methods. In this work, we comparedsix recent single image HDR reconstruction (SI-HDR) methodsin a subjective image quality experiment on an HDR display. Wefound that only two methods produced results that are, on average,more preferred than the unprocessed single exposure images. Whenthe same methods are evaluated using image quality metrics, astypically done in papers, the metric predictions correlate poorlywith subjective quality scores. The main reason is a significant toneand color difference between the reference and reconstructed HDRimages. To improve the predictions of image quality metrics, we propose correcting for the inaccuracies of the estimated cameraresponse curve before computing quality values. We further analyzethe sources of prediction noise when evaluating SI-HDR methodsand demonstrate that existing metrics can reliably predict onlylarge quality differences.

Place, publisher, year, edition, pages
New York, NY, United States: Association for Computing Machinery (ACM), 2022. p. 1-8, article id 1
Keywords [en]
High dynamic range, inverse problems, image quality metrics
National Category
Media and Communication Technology
Identifiers
URN: urn:nbn:se:liu:diva-186401DOI: 10.1145/3528233.3530729ISBN: 9781450393379 (print)OAI: oai:DiVA.org:liu-186401DiVA, id: diva2:1676041
Conference
SIGGRAPH '22: Special Interest Group on Computer Graphics and Interactive Techniques Conference Vancouver BC Canada August 7 - 11, 2022
Note

Funding: This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement N° 725253–EyeCode)

Available from: 2022-06-23 Created: 2022-06-23 Last updated: 2024-08-26Bibliographically approved

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Eilertsen, GabrielHajisharif, SaghiUnger, Jonas

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