2013
Brislawn, Christopher M.
On the group-theoretic structure of lifted filter banks Book Chapter
In: Andrews, Travis; Balan, Radu; Benedetto, John; Czaja, Wojciech; Okoudjou, Kasso (Ed.): Excursions in Harmonic Analysis, vol.~2, pp. 113-135, Birkh, Basel, 2013, (LA-UR-12-21217).
Abstract | Links | BibTeX | Tags: Filter bank, Group lift- ing structure, Group theory, group-theoretic structure, JPEG 2000, lifted filter banks, Lifting, Linear phase filter, Matrix polynomial, Polyphase matrix, Unique factorization, Wavelet
@inbook{Brislawn2013,
title = {On the group-theoretic structure of lifted filter banks},
author = {Christopher M. Brislawn},
editor = {Travis Andrews and Radu Balan and John Benedetto and Wojciech Czaja and Kasso Okoudjou},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/OnTheGroup-TheoreticStructureOfLiftedFilterBanks.pdf},
doi = {10.1007/978-0-8176-8379-5_6},
year = {2013},
date = {2013-01-01},
booktitle = {Excursions in Harmonic Analysis, vol.~2},
pages = {113-135},
publisher = {Birkh},
address = {Basel},
series = {Applied and Numerical Harmonic Analysis},
abstract = {The polyphase-with-advance matrix representations of whole-sample symmetric (WS) unimodular filter banks form a multiplicative matrix Laurent poly- nomial group. Elements of this group can always be factored into lifting matrices with half-sample symmetric (HS) off-diagonal lifting filters; such linear phase lift- ing factorizations are specified in the ISO/IEC JPEG 2000 image coding standard. Half-sample symmetric unimodular filter banks do not form a group, but such filter banks can be partially factored into a cascade of whole-sample antisymmetric (WA) lifting matrices starting from a concentric, equal-length HS base filter bank. An al- gebraic framework called a group lifting structure has been introduced to formalize the group-theoretic aspects of matrix lifting factorizations. Despite their pronounced differences, it has been shown that the group lifting structures for both the WS and HS classes satisfy a polyphase order-increasing property that implies uniqueness (“modulo rescaling”) of irreducible group lifting factorizations in both group lifting structures. These unique factorization results can in turn be used to characterize the group-theoretic structure of the groups generated by the WS and HS group lifting structures.},
note = {LA-UR-12-21217},
keywords = {Filter bank, Group lift- ing structure, Group theory, group-theoretic structure, JPEG 2000, lifted filter banks, Lifting, Linear phase filter, Matrix polynomial, Polyphase matrix, Unique factorization, Wavelet},
pubstate = {published},
tppubtype = {inbook}
}
The polyphase-with-advance matrix representations of whole-sample symmetric (WS) unimodular filter banks form a multiplicative matrix Laurent poly- nomial group. Elements of this group can always be factored into lifting matrices with half-sample symmetric (HS) off-diagonal lifting filters; such linear phase lift- ing factorizations are specified in the ISO/IEC JPEG 2000 image coding standard. Half-sample symmetric unimodular filter banks do not form a group, but such filter banks can be partially factored into a cascade of whole-sample antisymmetric (WA) lifting matrices starting from a concentric, equal-length HS base filter bank. An al- gebraic framework called a group lifting structure has been introduced to formalize the group-theoretic aspects of matrix lifting factorizations. Despite their pronounced differences, it has been shown that the group lifting structures for both the WS and HS classes satisfy a polyphase order-increasing property that implies uniqueness (“modulo rescaling”) of irreducible group lifting factorizations in both group lifting structures. These unique factorization results can in turn be used to characterize the group-theoretic structure of the groups generated by the WS and HS group lifting structures.
2012
Brislawn, Christopher M.; Woodring, Jonathan; Mniszewski, Susan; DeMarle, David; Ahrens, James
Subband coding for large-scale scientific simulation data using JPEG 2000 Proceedings Article
In: Image Analysis and Interpretation (SSIAI), 2012 IEEE Southwest Symposium on, pp. 201–204, IEEE 2012, (LA-UR-12-1352).
Abstract | Links | BibTeX | Tags: JPEG 2000, scientific simulation data, subband coding
@inproceedings{brislawn2012subband,
title = {Subband coding for large-scale scientific simulation data using JPEG 2000},
author = {Christopher M. Brislawn and Jonathan Woodring and Susan Mniszewski and David DeMarle and James Ahrens},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/SubbandCodingForLarge-ScaleScientificSimulationDataUsingJPEG2000.pdf},
year = {2012},
date = {2012-01-01},
booktitle = {Image Analysis and Interpretation (SSIAI), 2012 IEEE Southwest Symposium on},
pages = {201--204},
organization = {IEEE},
abstract = {The ISO/IEC JPEG 2000 image coding standard is a family of source coding algorithms targeting high-resolution image communications. JPEG 2000 features highly scalable embedded coding features that allow one to interactively zoom out to reduced resolution thumbnails of enormous data sets or to zoom in on highly localized regions of interest with very economical communications and rendering requirements. While intended for fixed-precision input data, the implementation of the irreversible version of the standard is often done internally in floating point arithmetic. Moreover, the standard is designed to support high-bit-depth data. Part 2 of the standard also provides support for three-dimensional data sets such as multicomponent or volumetric imagery. These features make JPEG 2000 an appealing candidate for highly scalable communications coding and visualization of two- and three-dimensional data produced by scientific simulation software. We present results of initial experiments applying JPEG 2000 to scientific simulation data produced by the Parallel Ocean Program (POP) global ocean circulation model, highlighting both the promise and the many challenges this approach holds for scientific visualization applications.},
note = {LA-UR-12-1352},
keywords = {JPEG 2000, scientific simulation data, subband coding},
pubstate = {published},
tppubtype = {inproceedings}
}
The ISO/IEC JPEG 2000 image coding standard is a family of source coding algorithms targeting high-resolution image communications. JPEG 2000 features highly scalable embedded coding features that allow one to interactively zoom out to reduced resolution thumbnails of enormous data sets or to zoom in on highly localized regions of interest with very economical communications and rendering requirements. While intended for fixed-precision input data, the implementation of the irreversible version of the standard is often done internally in floating point arithmetic. Moreover, the standard is designed to support high-bit-depth data. Part 2 of the standard also provides support for three-dimensional data sets such as multicomponent or volumetric imagery. These features make JPEG 2000 an appealing candidate for highly scalable communications coding and visualization of two- and three-dimensional data produced by scientific simulation software. We present results of initial experiments applying JPEG 2000 to scientific simulation data produced by the Parallel Ocean Program (POP) global ocean circulation model, highlighting both the promise and the many challenges this approach holds for scientific visualization applications.
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.
Brislawn, Christopher M.
On the group-theoretic structure of lifted filter banks Book Chapter
In: Andrews, Travis; Balan, Radu; Benedetto, John; Czaja, Wojciech; Okoudjou, Kasso (Ed.): Excursions in Harmonic Analysis, vol.~2, pp. 113-135, Birkh, Basel, 2013, (LA-UR-12-21217).
@inbook{Brislawn2013,
title = {On the group-theoretic structure of lifted filter banks},
author = {Christopher M. Brislawn},
editor = {Travis Andrews and Radu Balan and John Benedetto and Wojciech Czaja and Kasso Okoudjou},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/OnTheGroup-TheoreticStructureOfLiftedFilterBanks.pdf},
doi = {10.1007/978-0-8176-8379-5_6},
year = {2013},
date = {2013-01-01},
booktitle = {Excursions in Harmonic Analysis, vol.~2},
pages = {113-135},
publisher = {Birkh},
address = {Basel},
series = {Applied and Numerical Harmonic Analysis},
abstract = {The polyphase-with-advance matrix representations of whole-sample symmetric (WS) unimodular filter banks form a multiplicative matrix Laurent poly- nomial group. Elements of this group can always be factored into lifting matrices with half-sample symmetric (HS) off-diagonal lifting filters; such linear phase lift- ing factorizations are specified in the ISO/IEC JPEG 2000 image coding standard. Half-sample symmetric unimodular filter banks do not form a group, but such filter banks can be partially factored into a cascade of whole-sample antisymmetric (WA) lifting matrices starting from a concentric, equal-length HS base filter bank. An al- gebraic framework called a group lifting structure has been introduced to formalize the group-theoretic aspects of matrix lifting factorizations. Despite their pronounced differences, it has been shown that the group lifting structures for both the WS and HS classes satisfy a polyphase order-increasing property that implies uniqueness (“modulo rescaling”) of irreducible group lifting factorizations in both group lifting structures. These unique factorization results can in turn be used to characterize the group-theoretic structure of the groups generated by the WS and HS group lifting structures.},
note = {LA-UR-12-21217},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
The polyphase-with-advance matrix representations of whole-sample symmetric (WS) unimodular filter banks form a multiplicative matrix Laurent poly- nomial group. Elements of this group can always be factored into lifting matrices with half-sample symmetric (HS) off-diagonal lifting filters; such linear phase lift- ing factorizations are specified in the ISO/IEC JPEG 2000 image coding standard. Half-sample symmetric unimodular filter banks do not form a group, but such filter banks can be partially factored into a cascade of whole-sample antisymmetric (WA) lifting matrices starting from a concentric, equal-length HS base filter bank. An al- gebraic framework called a group lifting structure has been introduced to formalize the group-theoretic aspects of matrix lifting factorizations. Despite their pronounced differences, it has been shown that the group lifting structures for both the WS and HS classes satisfy a polyphase order-increasing property that implies uniqueness (“modulo rescaling”) of irreducible group lifting factorizations in both group lifting structures. These unique factorization results can in turn be used to characterize the group-theoretic structure of the groups generated by the WS and HS group lifting structures.
2.
Brislawn, Christopher M.; Woodring, Jonathan; Mniszewski, Susan; DeMarle, David; Ahrens, James
Subband coding for large-scale scientific simulation data using JPEG 2000 Proceedings Article
In: Image Analysis and Interpretation (SSIAI), 2012 IEEE Southwest Symposium on, pp. 201–204, IEEE 2012, (LA-UR-12-1352).
@inproceedings{brislawn2012subband,
title = {Subband coding for large-scale scientific simulation data using JPEG 2000},
author = {Christopher M. Brislawn and Jonathan Woodring and Susan Mniszewski and David DeMarle and James Ahrens},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/SubbandCodingForLarge-ScaleScientificSimulationDataUsingJPEG2000.pdf},
year = {2012},
date = {2012-01-01},
booktitle = {Image Analysis and Interpretation (SSIAI), 2012 IEEE Southwest Symposium on},
pages = {201--204},
organization = {IEEE},
abstract = {The ISO/IEC JPEG 2000 image coding standard is a family of source coding algorithms targeting high-resolution image communications. JPEG 2000 features highly scalable embedded coding features that allow one to interactively zoom out to reduced resolution thumbnails of enormous data sets or to zoom in on highly localized regions of interest with very economical communications and rendering requirements. While intended for fixed-precision input data, the implementation of the irreversible version of the standard is often done internally in floating point arithmetic. Moreover, the standard is designed to support high-bit-depth data. Part 2 of the standard also provides support for three-dimensional data sets such as multicomponent or volumetric imagery. These features make JPEG 2000 an appealing candidate for highly scalable communications coding and visualization of two- and three-dimensional data produced by scientific simulation software. We present results of initial experiments applying JPEG 2000 to scientific simulation data produced by the Parallel Ocean Program (POP) global ocean circulation model, highlighting both the promise and the many challenges this approach holds for scientific visualization applications.},
note = {LA-UR-12-1352},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The ISO/IEC JPEG 2000 image coding standard is a family of source coding algorithms targeting high-resolution image communications. JPEG 2000 features highly scalable embedded coding features that allow one to interactively zoom out to reduced resolution thumbnails of enormous data sets or to zoom in on highly localized regions of interest with very economical communications and rendering requirements. While intended for fixed-precision input data, the implementation of the irreversible version of the standard is often done internally in floating point arithmetic. Moreover, the standard is designed to support high-bit-depth data. Part 2 of the standard also provides support for three-dimensional data sets such as multicomponent or volumetric imagery. These features make JPEG 2000 an appealing candidate for highly scalable communications coding and visualization of two- and three-dimensional data produced by scientific simulation software. We present results of initial experiments applying JPEG 2000 to scientific simulation data produced by the Parallel Ocean Program (POP) global ocean circulation model, highlighting both the promise and the many challenges this approach holds for scientific visualization applications.
3.
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.