Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

8-2020

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 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

Keywords

Challenge; Extreme super-resolution; Video enhancement; Video restoration

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of the 16th European Conference on Computer Vision, Glasgow, Scotland, 2020 August 23-28

First Page

57

Last Page

81

ISBN

9783030668228

Identifier

10.1007/978-3-030-66823-5_4

Publisher

Springer Science and Business Media Deutschland GmbH

City or Country

Switzerland

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