Application-driven compression for visualizing large-scale time-varying data

Chaoli Wang; Hongfeng Yu; Kwan Liu Ma

doi:10.1109/MCG.2010.3

Application-driven compression for visualizing large-scale time-varying data

Chaoli Wang, Hongfeng Yu, Kwan Liu Ma

Research output: Contribution to journal › Article › peer-review

39 Scopus citations

Abstract

The authors present an application-driven approach to compressing large-scale time-varying volume data. Their approach identifies a reference feature to partition the data into space-time blocks, which are compressed with various precisions depending on their association to the feature. Runtime decompression is performed with bit-wise texture packing and deferred filtering. This method achieves high compression rates and interactive rendering while preserving fine details surrounding regions of interest. Such an application-driven approach could help computational scientists cope with the large-data problem.

Original language	English (US)
Article number	5370743
Pages (from-to)	59-69
Number of pages	11
Journal	IEEE Computer Graphics and Applications
Volume	30
Issue number	1
DOIs	https://doi.org/10.1109/MCG.2010.3
State	Published - Jan 2010
Externally published	Yes

Keywords

Bit-wise texture packing
Computer graphics
Deferred filtering
Graphics and multimedia
Importance-based compression
Large-data visualization
Time-varying data visualization

ASJC Scopus subject areas

Software
Computer Graphics and Computer-Aided Design

Access to Document

10.1109/MCG.2010.3

Cite this

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abstract = "The authors present an application-driven approach to compressing large-scale time-varying volume data. Their approach identifies a reference feature to partition the data into space-time blocks, which are compressed with various precisions depending on their association to the feature. Runtime decompression is performed with bit-wise texture packing and deferred filtering. This method achieves high compression rates and interactive rendering while preserving fine details surrounding regions of interest. Such an application-driven approach could help computational scientists cope with the large-data problem.",

keywords = "Bit-wise texture packing, Computer graphics, Deferred filtering, Graphics and multimedia, Importance-based compression, Large-data visualization, Time-varying data visualization",

author = "Chaoli Wang and Hongfeng Yu and Ma, {Kwan Liu}",

note = "Funding Information: This research was supported in part by the US National Science Foundation{\textquoteright}s Information Technology Research program and the US Department of Energy{\textquoteright}s Scientific Discovery through Advanced Computing program. We thank Jacqueline H. Chen at Sandia National Laboratories for providing the combustion data set and sharing her domain knowledge. We also thank the reviewers for their constructive suggestions.",

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AU - Yu, Hongfeng

AU - Ma, Kwan Liu

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N2 - The authors present an application-driven approach to compressing large-scale time-varying volume data. Their approach identifies a reference feature to partition the data into space-time blocks, which are compressed with various precisions depending on their association to the feature. Runtime decompression is performed with bit-wise texture packing and deferred filtering. This method achieves high compression rates and interactive rendering while preserving fine details surrounding regions of interest. Such an application-driven approach could help computational scientists cope with the large-data problem.

AB - The authors present an application-driven approach to compressing large-scale time-varying volume data. Their approach identifies a reference feature to partition the data into space-time blocks, which are compressed with various precisions depending on their association to the feature. Runtime decompression is performed with bit-wise texture packing and deferred filtering. This method achieves high compression rates and interactive rendering while preserving fine details surrounding regions of interest. Such an application-driven approach could help computational scientists cope with the large-data problem.

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KW - Deferred filtering

KW - Graphics and multimedia

KW - Importance-based compression

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KW - Time-varying data visualization

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Application-driven compression for visualizing large-scale time-varying data

Abstract

Keywords

ASJC Scopus subject areas

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