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Issue No. 01 - January/February (2010 vol. 30)
ISSN: 0272-1716
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.
large-data visualization, time-varying data visualization, importance-based compression, bit-wise texture packing, deferred filtering, computer graphics, graphics and multimedia

H. Yu, K. Ma and C. Wang, "Application-Driven Compression for Visualizing Large-Scale Time-Varying Data," in IEEE Computer Graphics and Applications, vol. 30, no. , pp. 59-69, 2010.
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