ERQA: Edge-restoration Quality Assessment for Video Super-Resolution

Anastasia Kirillova, Eugene Lyapustin, Anastasia Antsiferova, Dmitry Vatolin

2022

Abstract

Despite the growing popularity of video super-resolution (VSR), there is still no good way to assess the quality of the restored details in upscaled frames. Some VSR methods may produce the wrong digit or an entirely different face. Whether a method’s results are trustworthy depends on how well it restores truthful details. Image super-resolution can use natural distributions to produce a high-resolution image that is only somewhat similar to the real one. VSR enables exploration of additional information in neighboring frames to restore details from the original scene. The ERQA metric, which we propose in this paper, aims to estimate a model’s ability to restore real details using VSR. On the assumption that edges are significant for detail and character recognition, we chose edge fidelity as the foundation for this metric. Experimental validation of our work is based on the MSU Video Super-Resolution Benchmark, which includes the most difficult patterns for detail restoration and verifies the fidelity of details from the original frame. Code for the proposed metric is publicly available at https://github.com/msu-video-group/ERQA.

Paper Citation

in Harvard Style

Kirillova A., Lyapustin E., Antsiferova A. and Vatolin D. (2022). ERQA: Edge-restoration Quality Assessment for Video Super-Resolution. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP; ISBN 978-989-758-555-5, SciTePress, pages 315-322. DOI: 10.5220/0010780900003124

in Bibtex Style

@conference{visapp22,
author={Anastasia Kirillova and Eugene Lyapustin and Anastasia Antsiferova and Dmitry Vatolin},
title={ERQA: Edge-restoration Quality Assessment for Video Super-Resolution},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP},
year={2022},
pages={315-322},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010780900003124},
isbn={978-989-758-555-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP
TI - ERQA: Edge-restoration Quality Assessment for Video Super-Resolution
SN - 978-989-758-555-5
AU - Kirillova A.
AU - Lyapustin E.
AU - Antsiferova A.
AU - Vatolin D.
PY - 2022
SP - 315
EP - 322
DO - 10.5220/0010780900003124
PB - SciTePress