AIM 2020 challenge on video extreme super-resolution: methods and results
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 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 low-resolution (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.
57–81
Fuoli, Dario
0f4b3991-0e64-4cad-8eed-1af5111fdc4b
Huang, Zhiwu
84f477cd-9097-44dd-a33e-ff71f253d36b
Timofte, Radu
848d4025-8613-43f3-92b7-4b4a2b29711a
Fuoli, Dario
0f4b3991-0e64-4cad-8eed-1af5111fdc4b
Huang, Zhiwu
84f477cd-9097-44dd-a33e-ff71f253d36b
Timofte, Radu
848d4025-8613-43f3-92b7-4b4a2b29711a
Fuoli, Dario, Huang, Zhiwu and Timofte, Radu
(2021)
AIM 2020 challenge on video extreme super-resolution: methods and results.
European Conference on Computer Vision (ECCV) workshop.
.
(doi:10.1007/978-3-030-66823-5_4).
Record type:
Conference or Workshop Item
(Paper)
Abstract
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 low-resolution (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.
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e-pub ahead of print date: 31 January 2021
Venue - Dates:
European Conference on Computer Vision (ECCV) workshop, 2020-08-23
Identifiers
Local EPrints ID: 501685
URI: http://eprints.soton.ac.uk/id/eprint/501685
PURE UUID: 8f4d2ecb-9951-4610-bd35-1f938cd69598
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Date deposited: 05 Jun 2025 16:58
Last modified: 06 Jun 2025 02:06
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Contributors
Author:
Dario Fuoli
Author:
Zhiwu Huang
Author:
Radu Timofte
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