AIM 2020 challenge on video extreme super-resolution: methods and results

This paper reviews the video extreme super-resolution challenge associated with the AIM 2020 workshop at ECCV 2020. Common scaling factors for learned video super-resolution (VSR) do not go beyond factor 4. Missing information can be restored well in this region, especially in HR videos, where the h...

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Main Authors: FUOLI, D., HUANG, Zhiwu, GU, S., TIMOFTE, R., RAVENTOS, A., ESFANDIARI, A., KAROUT, S., XU, X., LI, X., XIONG, X., WANG, J., NAVARRETE, Michelini P., ZHANG, W., ZHANG, D., ZHU, H., XIA, D., CHEN, H., GU, J., ZHANG, Z., ZHAO, T.
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Language:English
Published: Institutional Knowledge at Singapore Management University 2020
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Online Access:https://ink.library.smu.edu.sg/sis_research/6549
https://ink.library.smu.edu.sg/context/sis_research/article/7552/viewcontent/AIM_2020_Challenge_on_Video_Extreme_Super_Resoluti.pdf
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spelling sg-smu-ink.sis_research-75522022-01-10T03:39:34Z AIM 2020 challenge on video extreme super-resolution: methods and results FUOLI, D. HUANG, Zhiwu GU, S. TIMOFTE, R. RAVENTOS, A. ESFANDIARI, A. KAROUT, S. XU, X. LI, X. XIONG, X. WANG, J. NAVARRETE, Michelini P. ZHANG, W. ZHANG, D. ZHU, H. XIA, D. CHEN, H. GU, J. ZHANG, Z. ZHAO, T. This paper reviews the video extreme super-resolution challenge associated with the AIM 2020 workshop at ECCV 2020. Common scaling factors for learned video super-resolution (VSR) do not go beyond factor 4. Missing information can be restored well in this region, especially in HR videos, where the high-frequency content mostly consists of texture details. The task in this challenge is to upscale videos with an extreme factor of 16, which results in more serious degradations that also affect the structural integrity of the videos. A single pixel in the lowresolution (LR) domain corresponds to 256 pixels in the high-resolution (HR) domain. Due to this massive information loss, it is hard to accurately restore the missing information. Track 1 is set up to gauge the state-of-the-art for such a demanding task, where fidelity to the ground truth is measured by PSNR and SSIM. Perceptually higher quality can be achieved in trade-off for fidelity by generating plausible high-frequency content. Track 2 therefore aims at generating visually pleasing results, which are ranked according to human perception, evaluated by a user study. In contrast to single image super-resolution (SISR), VSR can benefit from additional information in the temporal domain. However, this also imposes an additional requirement, as the generated frames need to be consistent along time 2020-08-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/6549 info:doi/10.1007/978-3-030-66823-5_4 https://ink.library.smu.edu.sg/context/sis_research/article/7552/viewcontent/AIM_2020_Challenge_on_Video_Extreme_Super_Resoluti.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Challenge; Extreme super-resolution; Video enhancement; Video restoration Databases and Information Systems
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Challenge; Extreme super-resolution; Video enhancement; Video restoration
Databases and Information Systems
spellingShingle Challenge; Extreme super-resolution; Video enhancement; Video restoration
Databases and Information Systems
FUOLI, D.
HUANG, Zhiwu
GU, S.
TIMOFTE, R.
RAVENTOS, A.
ESFANDIARI, A.
KAROUT, S.
XU, X.
LI, X.
XIONG, X.
WANG, J.
NAVARRETE, Michelini P.
ZHANG, W.
ZHANG, D.
ZHU, H.
XIA, D.
CHEN, H.
GU, J.
ZHANG, Z.
ZHAO, T.
AIM 2020 challenge on video extreme super-resolution: methods and results
description This paper reviews the video extreme super-resolution challenge associated with the AIM 2020 workshop at ECCV 2020. Common scaling factors for learned video super-resolution (VSR) do not go beyond factor 4. Missing information can be restored well in this region, especially in HR videos, where the high-frequency content mostly consists of texture details. The task in this challenge is to upscale videos with an extreme factor of 16, which results in more serious degradations that also affect the structural integrity of the videos. A single pixel in the lowresolution (LR) domain corresponds to 256 pixels in the high-resolution (HR) domain. Due to this massive information loss, it is hard to accurately restore the missing information. Track 1 is set up to gauge the state-of-the-art for such a demanding task, where fidelity to the ground truth is measured by PSNR and SSIM. Perceptually higher quality can be achieved in trade-off for fidelity by generating plausible high-frequency content. Track 2 therefore aims at generating visually pleasing results, which are ranked according to human perception, evaluated by a user study. In contrast to single image super-resolution (SISR), VSR can benefit from additional information in the temporal domain. However, this also imposes an additional requirement, as the generated frames need to be consistent along time
format text
author FUOLI, D.
HUANG, Zhiwu
GU, S.
TIMOFTE, R.
RAVENTOS, A.
ESFANDIARI, A.
KAROUT, S.
XU, X.
LI, X.
XIONG, X.
WANG, J.
NAVARRETE, Michelini P.
ZHANG, W.
ZHANG, D.
ZHU, H.
XIA, D.
CHEN, H.
GU, J.
ZHANG, Z.
ZHAO, T.
author_facet FUOLI, D.
HUANG, Zhiwu
GU, S.
TIMOFTE, R.
RAVENTOS, A.
ESFANDIARI, A.
KAROUT, S.
XU, X.
LI, X.
XIONG, X.
WANG, J.
NAVARRETE, Michelini P.
ZHANG, W.
ZHANG, D.
ZHU, H.
XIA, D.
CHEN, H.
GU, J.
ZHANG, Z.
ZHAO, T.
author_sort FUOLI, D.
title AIM 2020 challenge on video extreme super-resolution: methods and results
title_short AIM 2020 challenge on video extreme super-resolution: methods and results
title_full AIM 2020 challenge on video extreme super-resolution: methods and results
title_fullStr AIM 2020 challenge on video extreme super-resolution: methods and results
title_full_unstemmed AIM 2020 challenge on video extreme super-resolution: methods and results
title_sort aim 2020 challenge on video extreme super-resolution: methods and results
publisher Institutional Knowledge at Singapore Management University
publishDate 2020
url https://ink.library.smu.edu.sg/sis_research/6549
https://ink.library.smu.edu.sg/context/sis_research/article/7552/viewcontent/AIM_2020_Challenge_on_Video_Extreme_Super_Resoluti.pdf
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