Video Compression for Ocean Simulation Image Databases
Climate research requires monitoring a large range of spatial and temporal scales to understand the climate system and potential future impacts. Climate simulations are now run with very high resolution (1-10 km gridcells) ocean, sea ice, and atmosphere components, and can easily produce petabytes o...
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The Eurographics Association
2017
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Online Access: | https://dx.doi.org/10.2312/envirvis.20171104 https://diglib.eg.org/handle/10.2312/envirvis20171104 |
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ftdatacite:10.2312/envirvis.20171104 2023-05-15T18:18:34+02:00 Video Compression for Ocean Simulation Image Databases Berres, Anne S. Turton, Terece L. Petersen, Mark Rogers, David H. Ahrens, James P. 2017 https://dx.doi.org/10.2312/envirvis.20171104 https://diglib.eg.org/handle/10.2312/envirvis20171104 unknown The Eurographics Association https://diglib.eg.org/bitstream/handle/10.2312/envirvis20171104/049-053.pdf Data Storage Representations [E.2.5] Data Representation Image Processing and Computer Vision [I.4.2] Compression Coding CreativeWork article 2017 ftdatacite https://doi.org/10.2312/envirvis.20171104 2021-11-05T12:55:41Z Climate research requires monitoring a large range of spatial and temporal scales to understand the climate system and potential future impacts. Climate simulations are now run with very high resolution (1-10 km gridcells) ocean, sea ice, and atmosphere components, and can easily produce petabytes of output. This overloads storage systems and hinders visualization and analysis. Image databases can decrease storage sizes from petabytes of simulation output down to several hundred gigabytes of images. In this paper, we introduce video compression as a method to further decrease database sizes by 2-4 orders of magnitude. We compare compression and access speeds, compressed sizes, and compression quality over a range of settings. Quality is assessed through image quality metrics and expert feedback. Overall, we were able to show that video compression techniques provide an efficient means of storing image databases at a shareable size, while preserving image quality. This enables the wise use of available disk space, so scientists can more easily study the physical features of interest. : Workshop on Visualisation in Environmental Sciences (EnvirVis) : Session 3 : 49 : 53 : Anne S. Berres, Terece L. Turton, Mark Petersen, David H. Rogers, and James P. Ahrens : Categories and Subject Descriptors (according to ACM CCS): Data Storage Representations [E.2.5]: Data Representation-; Image Processing and Computer Vision [I.4.2]: Compression (Coding) Article in Journal/Newspaper Sea ice DataCite Metadata Store (German National Library of Science and Technology) Petersen ENVELOPE(-101.250,-101.250,-71.917,-71.917) |
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Open Polar |
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DataCite Metadata Store (German National Library of Science and Technology) |
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topic |
Data Storage Representations [E.2.5] Data Representation Image Processing and Computer Vision [I.4.2] Compression Coding |
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Data Storage Representations [E.2.5] Data Representation Image Processing and Computer Vision [I.4.2] Compression Coding Berres, Anne S. Turton, Terece L. Petersen, Mark Rogers, David H. Ahrens, James P. Video Compression for Ocean Simulation Image Databases |
topic_facet |
Data Storage Representations [E.2.5] Data Representation Image Processing and Computer Vision [I.4.2] Compression Coding |
description |
Climate research requires monitoring a large range of spatial and temporal scales to understand the climate system and potential future impacts. Climate simulations are now run with very high resolution (1-10 km gridcells) ocean, sea ice, and atmosphere components, and can easily produce petabytes of output. This overloads storage systems and hinders visualization and analysis. Image databases can decrease storage sizes from petabytes of simulation output down to several hundred gigabytes of images. In this paper, we introduce video compression as a method to further decrease database sizes by 2-4 orders of magnitude. We compare compression and access speeds, compressed sizes, and compression quality over a range of settings. Quality is assessed through image quality metrics and expert feedback. Overall, we were able to show that video compression techniques provide an efficient means of storing image databases at a shareable size, while preserving image quality. This enables the wise use of available disk space, so scientists can more easily study the physical features of interest. : Workshop on Visualisation in Environmental Sciences (EnvirVis) : Session 3 : 49 : 53 : Anne S. Berres, Terece L. Turton, Mark Petersen, David H. Rogers, and James P. Ahrens : Categories and Subject Descriptors (according to ACM CCS): Data Storage Representations [E.2.5]: Data Representation-; Image Processing and Computer Vision [I.4.2]: Compression (Coding) |
format |
Article in Journal/Newspaper |
author |
Berres, Anne S. Turton, Terece L. Petersen, Mark Rogers, David H. Ahrens, James P. |
author_facet |
Berres, Anne S. Turton, Terece L. Petersen, Mark Rogers, David H. Ahrens, James P. |
author_sort |
Berres, Anne S. |
title |
Video Compression for Ocean Simulation Image Databases |
title_short |
Video Compression for Ocean Simulation Image Databases |
title_full |
Video Compression for Ocean Simulation Image Databases |
title_fullStr |
Video Compression for Ocean Simulation Image Databases |
title_full_unstemmed |
Video Compression for Ocean Simulation Image Databases |
title_sort |
video compression for ocean simulation image databases |
publisher |
The Eurographics Association |
publishDate |
2017 |
url |
https://dx.doi.org/10.2312/envirvis.20171104 https://diglib.eg.org/handle/10.2312/envirvis20171104 |
long_lat |
ENVELOPE(-101.250,-101.250,-71.917,-71.917) |
geographic |
Petersen |
geographic_facet |
Petersen |
genre |
Sea ice |
genre_facet |
Sea ice |
op_relation |
https://diglib.eg.org/bitstream/handle/10.2312/envirvis20171104/049-053.pdf |
op_doi |
https://doi.org/10.2312/envirvis.20171104 |
_version_ |
1766195179008557056 |