2019
Baker, Allison; Hammerling, Dorit; Turton, Terece
Evaluating Image Quality Measures to Assess the Impact of Lossy Data Compression Applied to Climate Simulation Data Journal Article
In: Computer Graphics Forum, 2019, ISSN: 1467-8659, (LA-UR-19-22420).
Links | BibTeX | Tags: data compaction and compression, feature evaluation
@article{10.1111:cgf.13707,
title = {Evaluating Image Quality Measures to Assess the Impact of Lossy Data Compression Applied to Climate Simulation Data},
author = {Allison Baker and Dorit Hammerling and Terece Turton},
url = {https://dsscale.org/wp-content/uploads/2019/07/BakerHammerlingTurtonEuroVis2019-EvalLossyCompression.pdf},
doi = {10.1111/cgf.13707},
issn = {1467-8659},
year = {2019},
date = {2019-01-01},
journal = {Computer Graphics Forum},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
note = {LA-UR-19-22420},
keywords = {data compaction and compression, feature evaluation},
pubstate = {published},
tppubtype = {article}
}
2011
Woodring, Jonathan; Mniszewski, Susan; Brislawn, Christopher M.; DeMarle, David; Ahrens, James
Revisiting wavelet compression for large-scale climate data using JPEG 2000 and ensuring data precision Proceedings Article
In: Large Data Analysis and Visualization (LDAV), 2011 IEEE Symposium on, pp. 31–38, IEEE 2011, (LA-UR-pending).
Abstract | Links | BibTeX | Tags: climate modeling, coding and information theory, data compaction and compression, JPEG 2000, Wavelet
@inproceedings{woodring2011revisiting,
title = {Revisiting wavelet compression for large-scale climate data using JPEG 2000 and ensuring data precision},
author = {Jonathan Woodring and Susan Mniszewski and Christopher M. Brislawn and David DeMarle and James Ahrens},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/RevisitingWaveletComp.pdf},
year = {2011},
date = {2011-01-01},
booktitle = {Large Data Analysis and Visualization (LDAV), 2011 IEEE Symposium on},
pages = {31--38},
organization = {IEEE},
abstract = {We revisit wavelet compression by using a standards-based method to reduce large-scale data sizes for production scientific computing. Many of the bottlenecks in visualization and analysis come from limited bandwidth in data movement, from storage to networks. The majority of the processing time for visualization and analysis is spent reading or writing large-scale data or moving data from a remote site in a distance scenario. Using wavelet compression in JPEG 2000, we provide a mechanism to vary data transfer time versus data quality, so that a domain expert can improve data transfer time while quantifying compression effects on their data. By using a standards-based method, we are able to provide scientists with the state-of-the-art wavelet compression from the signal processing and data compression community, suitable for use in a production computing environment. To quantify compression effects, we focus on measuring bit rate versus maximum error as a quality metric to provide precision guarantees for scientific analysis on remotely compressed POP (Parallel Ocean Program) data.},
note = {LA-UR-pending},
keywords = {climate modeling, coding and information theory, data compaction and compression, JPEG 2000, Wavelet},
pubstate = {published},
tppubtype = {inproceedings}
}
We revisit wavelet compression by using a standards-based method to reduce large-scale data sizes for production scientific computing. Many of the bottlenecks in visualization and analysis come from limited bandwidth in data movement, from storage to networks. The majority of the processing time for visualization and analysis is spent reading or writing large-scale data or moving data from a remote site in a distance scenario. Using wavelet compression in JPEG 2000, we provide a mechanism to vary data transfer time versus data quality, so that a domain expert can improve data transfer time while quantifying compression effects on their data. By using a standards-based method, we are able to provide scientists with the state-of-the-art wavelet compression from the signal processing and data compression community, suitable for use in a production computing environment. To quantify compression effects, we focus on measuring bit rate versus maximum error as a quality metric to provide precision guarantees for scientific analysis on remotely compressed POP (Parallel Ocean Program) data.
: . .
1.
Baker, Allison; Hammerling, Dorit; Turton, Terece
Evaluating Image Quality Measures to Assess the Impact of Lossy Data Compression Applied to Climate Simulation Data Journal Article
In: Computer Graphics Forum, 2019, ISSN: 1467-8659, (LA-UR-19-22420).
@article{10.1111:cgf.13707,
title = {Evaluating Image Quality Measures to Assess the Impact of Lossy Data Compression Applied to Climate Simulation Data},
author = {Allison Baker and Dorit Hammerling and Terece Turton},
url = {https://dsscale.org/wp-content/uploads/2019/07/BakerHammerlingTurtonEuroVis2019-EvalLossyCompression.pdf},
doi = {10.1111/cgf.13707},
issn = {1467-8659},
year = {2019},
date = {2019-01-01},
journal = {Computer Graphics Forum},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
note = {LA-UR-19-22420},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2.
Woodring, Jonathan; Mniszewski, Susan; Brislawn, Christopher M.; DeMarle, David; Ahrens, James
Revisiting wavelet compression for large-scale climate data using JPEG 2000 and ensuring data precision Proceedings Article
In: Large Data Analysis and Visualization (LDAV), 2011 IEEE Symposium on, pp. 31–38, IEEE 2011, (LA-UR-pending).
@inproceedings{woodring2011revisiting,
title = {Revisiting wavelet compression for large-scale climate data using JPEG 2000 and ensuring data precision},
author = {Jonathan Woodring and Susan Mniszewski and Christopher M. Brislawn and David DeMarle and James Ahrens},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/RevisitingWaveletComp.pdf},
year = {2011},
date = {2011-01-01},
booktitle = {Large Data Analysis and Visualization (LDAV), 2011 IEEE Symposium on},
pages = {31--38},
organization = {IEEE},
abstract = {We revisit wavelet compression by using a standards-based method to reduce large-scale data sizes for production scientific computing. Many of the bottlenecks in visualization and analysis come from limited bandwidth in data movement, from storage to networks. The majority of the processing time for visualization and analysis is spent reading or writing large-scale data or moving data from a remote site in a distance scenario. Using wavelet compression in JPEG 2000, we provide a mechanism to vary data transfer time versus data quality, so that a domain expert can improve data transfer time while quantifying compression effects on their data. By using a standards-based method, we are able to provide scientists with the state-of-the-art wavelet compression from the signal processing and data compression community, suitable for use in a production computing environment. To quantify compression effects, we focus on measuring bit rate versus maximum error as a quality metric to provide precision guarantees for scientific analysis on remotely compressed POP (Parallel Ocean Program) data.},
note = {LA-UR-pending},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We revisit wavelet compression by using a standards-based method to reduce large-scale data sizes for production scientific computing. Many of the bottlenecks in visualization and analysis come from limited bandwidth in data movement, from storage to networks. The majority of the processing time for visualization and analysis is spent reading or writing large-scale data or moving data from a remote site in a distance scenario. Using wavelet compression in JPEG 2000, we provide a mechanism to vary data transfer time versus data quality, so that a domain expert can improve data transfer time while quantifying compression effects on their data. By using a standards-based method, we are able to provide scientists with the state-of-the-art wavelet compression from the signal processing and data compression community, suitable for use in a production computing environment. To quantify compression effects, we focus on measuring bit rate versus maximum error as a quality metric to provide precision guarantees for scientific analysis on remotely compressed POP (Parallel Ocean Program) data.