Physics based Motion Estimation to Improve Video Compression

James McCullough, Naseer Al-Jawad, Tuan Nguyen

2022

Abstract

Optical flow is a fundamental component of video compression as it can be used to effectively compress sequential frames. However, currently optical flow is only a transformation of one frame into another. This paper considers the possibility of representing optical flow based on physics principles which has not, to our knowledge, been researched before. Video often consists of real-world events captured by a camera, meaning that objects within videos follow Newtonian physics, so the video can be compressed by converting the motion of the object into physics-based motion paths. The proposed algorithm converts an object’s location over a series of frames into a sequence of physics motion paths. The space cost in saving these motion paths could be considerably smaller compared with traditional optical flow, and this improves video compression in exchange for increased encoding/decoding times. Based on our experimental implementation, motion paths can be used to compress the motion of objects on basic trajectories. By comparing the file sizes between original and processed image sequences, effective compression on basic object movements can be identified.

Paper Citation

in Harvard Style

McCullough J., Al-Jawad N. and Nguyen T. (2022). Physics based Motion Estimation to Improve Video Compression. 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 364-371. DOI: 10.5220/0010811900003124

in Bibtex Style

@conference{visapp22,
author={James McCullough and Naseer Al-Jawad and Tuan Nguyen},
title={Physics based Motion Estimation to Improve Video Compression},
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={364-371},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010811900003124},
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 - Physics based Motion Estimation to Improve Video Compression
SN - 978-989-758-555-5
AU - McCullough J.
AU - Al-Jawad N.
AU - Nguyen T.
PY - 2022
SP - 364
EP - 371
DO - 10.5220/0010811900003124
PB - SciTePress