Deep Video Frame Rate Up-conversion Network using Feature-based Progressive Residue Refinement

Jinglei Shi, Xiaoran Jiang, Christine Guillemot

2022

Abstract

In this paper, we propose a deep learning-based network for video frame rate up-conversion (or video frame interpolation). The proposed optical flow-based pipeline employs deep features extracted to learn residue maps for progressively refining the synthesized intermediate frame. We also propose a procedure for fine- tuning the optical flow estimation module using frame interpolation datasets, which does not require ground truth optical flows. This procedure is effective to obtain interpolation task-oriented optical flows and can be applied to other methods utilizing a deep optical flow estimation module. Experimental results demonstrate that our proposed network performs favorably against state-of-the-art methods both in terms of qualitative and quantitative measures.

Paper Citation

in Harvard Style

Shi J., Jiang X. and Guillemot C. (2022). Deep Video Frame Rate Up-conversion Network using Feature-based Progressive Residue Refinement. 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 331-339. DOI: 10.5220/0010793900003124

in Bibtex Style

@conference{visapp22,
author={Jinglei Shi and Xiaoran Jiang and Christine Guillemot},
title={Deep Video Frame Rate Up-conversion Network using Feature-based Progressive Residue Refinement},
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={331-339},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010793900003124},
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 - Deep Video Frame Rate Up-conversion Network using Feature-based Progressive Residue Refinement
SN - 978-989-758-555-5
AU - Shi J.
AU - Jiang X.
AU - Guillemot C.
PY - 2022
SP - 331
EP - 339
DO - 10.5220/0010793900003124
PB - SciTePress