Application-driven compression for visualizing large-scale time-varying data
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. © 2010 IEEE.
IEEE Computer Graphics and Applications
Application-driven compression for visualizing large-scale time-varying data.
IEEE Computer Graphics and Applications,
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