The University of Southampton
University of Southampton Institutional Repository

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

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. 57–81 . (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.

This record has no associated files available for download.

More information

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
ORCID for Zhiwu Huang: ORCID iD orcid.org/0000-0002-7385-079X

Catalogue record

Date deposited: 05 Jun 2025 16:58
Last modified: 06 Jun 2025 02:06

Export record

Altmetrics

Contributors

Author: Dario Fuoli
Author: Zhiwu Huang ORCID iD
Author: Radu Timofte

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×