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Image and video compression of fluid flow data.

Authors :
Anatharaman, Vishal
Feldkamp, Jason
Fukami, Kai
Taira, Kunihiko
Source :
Theoretical & Computational Fluid Dynamics. Feb2023, Vol. 37 Issue 1, p61-82. 22p.
Publication Year :
2023

Abstract

We study the compression of spatial and temporal features in fluid flow data using multimedia compression techniques. The efficacy of spatial compression techniques, including JPEG and JPEG2000 (JP2), and spatiotemporal video compression techniques, namely H.264, H.265, and AV1, in limiting the introduction of compression artifacts and preserving underlying flow physics are considered for laminar periodic wake around a cylinder, two-dimensional turbulence, and turbulent channel flow. These compression techniques significantly compress flow data while maintaining dominant flow features with negligible error. AV1 and H.265 compressions present the best performance across a variety of canonical flow regimes and outperform traditional techniques such as proper orthogonal decomposition in some cases. These image and video compression algorithms are flexible, scalable, and generalizable holding potential for a wide range of applications in fluid dynamics in the context of data storage and transfer. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09354964
Volume :
37
Issue :
1
Database :
Academic Search Index
Journal :
Theoretical & Computational Fluid Dynamics
Publication Type :
Academic Journal
Accession number :
162507906
Full Text :
https://doi.org/10.1007/s00162-023-00643-4