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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Main Authors: Berres, Anne S., Turton, Terece L., Petersen, Mark, Rogers, David H., Ahrens, James P.
Format: Article in Journal/Newspaper
Language:unknown
Published: The Eurographics Association 2017
Subjects:
Online Access:https://dx.doi.org/10.2312/envirvis.20171104
https://diglib.eg.org/handle/10.2312/envirvis20171104
id ftdatacite:10.2312/envirvis.20171104
record_format openpolar
spelling 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)
institution Open Polar
collection DataCite Metadata Store (German National Library of Science and Technology)
op_collection_id ftdatacite
language unknown
topic Data Storage Representations [E.2.5]
Data Representation
Image Processing and Computer Vision [I.4.2]
Compression Coding
spellingShingle 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
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