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Authors: Arbind Agrahari Baniya ; Tsz-Kwan Lee ; Peter W. Eklund and Sunil Aryal

Affiliation: School of IT, Deakin University, Geelong, VIC, Australia

Keyword(s): High Definition Video, Image Analysis, Image Quality, Video Signal Processing, Super-resolution.

Abstract: Deep learning Video Super-Resolution (VSR) methods rely on learning spatio-temporal correlations between a target frame and its neighbouring frames in a given temporal radius to generate a high-resolution output. Among recent VSR models, a sliding window mechanism is popularly adopted by picking a fixed number of consecutive frames as neighbouring frames for a given target frame. This results in a single frame being used multiple times in the input space during the super-resolution process. Moreover, the approach of adopting the fixed consecutive frames directly does not allow deep learning models to learn the full extent of spatio-temporal inter-dependencies between a target frame and its neighbours along a video sequence. To mitigate these issues, this paper proposes a Spatio-Temporal Input Frame Selection (STIFS) algorithm based on image analysis to adaptively select the neighbouring frame(s) based on the spatio-temporal context dynamics with respect to the target frame. STIFS is first-ever dynamic selection mechanism proposed for VSR methods. It aims to enable VSR models to better learn spatio-temporal correlations in a given temporal radius and consequently maximise the quality of the high-definition output. The proposed STIFS algorithm achieved remarkable PSNR improvements in the high-resolution output for VSR models on benchmark datasets. (More)

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Paper citation in several formats:
Agrahari Baniya, A., Lee, T.-K., Eklund, P. W. and Aryal, S. (2022). STIFS: Spatio-Temporal Input Frame Selection for Learning-based Video Super-Resolution Models. In Proceedings of the 19th International Conference on Signal Processing and Multimedia Applications - SIGMAP; ISBN 978-989-758-591-3; ISSN 2184-9471, SciTePress, pages 48-58. DOI: 10.5220/0011339900003289

@conference{sigmap22,
author={Arbind {Agrahari Baniya} and Tsz{-}Kwan Lee and Peter W. Eklund and Sunil Aryal},
title={STIFS: Spatio-Temporal Input Frame Selection for Learning-based Video Super-Resolution Models},
booktitle={Proceedings of the 19th International Conference on Signal Processing and Multimedia Applications - SIGMAP},
year={2022},
pages={48-58},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011339900003289},
isbn={978-989-758-591-3},
issn={2184-9471},
}

TY - CONF

JO - Proceedings of the 19th International Conference on Signal Processing and Multimedia Applications - SIGMAP
TI - STIFS: Spatio-Temporal Input Frame Selection for Learning-based Video Super-Resolution Models
SN - 978-989-758-591-3
IS - 2184-9471
AU - Agrahari Baniya, A.
AU - Lee, T.
AU - Eklund, P.
AU - Aryal, S.
PY - 2022
SP - 48
EP - 58
DO - 10.5220/0011339900003289
PB - SciTePress

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