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Application-Driven Compression for Visualizing Large-Scale Time-Varying Data
January/February 2010 (vol. 30 no. 1)
pp. 59-69
Chaoli Wang, Michigan Technological University
Hongfeng Yu, Sandia National Laboratories
Kwan-Liu Ma, University of California, Davis
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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Index Terms:
large-data visualization, time-varying data visualization, importance-based compression, bit-wise texture packing, deferred filtering, computer graphics, graphics and multimedia
Citation:
Chaoli Wang, Hongfeng Yu, Kwan-Liu Ma, "Application-Driven Compression for Visualizing Large-Scale Time-Varying Data," IEEE Computer Graphics and Applications, vol. 30, no. 1, pp. 59-69, Jan.-Feb. 2010, doi:10.1109/MCG.2010.3
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