Application-Driven Compression for Visualizing Large-Scale Time-Varying Volume Data Export

@article{citeulike:5811327, abstract = {In many areas of science and engineering, the desires to study a problem at the highest possible resolution have led to an explosive growth of data. It is imperative to reduce the data to a manageable scale for analysis and visualization. For high-precision floating-point data, compressing the data solely based on values can only achieve a limited saving. Further reduction is possible with the fact that usually only a smaller subset of the data is of interest in analysis. In this paper, we present an application-driven approach to compressing large-scale time-varying volume data. Our method 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. We show that our method achieves high compression rates and interactive rendering while preserving fine details surrounding regions of interest. Such an application-driven approach points us to a promising direction for coping with the large data problems facing computational scientists.}, address = {Los Alamitos, CA, USA}, author = {Wang, Chaoli and Yu, Hongfeng and Ma, Kwan L.}, citeulike-article-id = {5811327}, citeulike-linkout-0 = {http://doi.ieeecomputersociety.org/10.1109/MCG.2009.104}, citeulike-linkout-1 = {http://dx.doi.org/10.1109/MCG.2009.104}, doi = {10.1109/MCG.2009.104}, issn = {0272-1716}, journal = {IEEE Computer Graphics and Applications}, keywords = {visualisation-3d, visualisation-algorithms, visualisation-scientific}, number = {1}, posted-at = {2009-09-21 08:28:48}, priority = {2}, publisher = {IEEE Computer Society}, title = {Application-Driven Compression for Visualizing Large-Scale Time-Varying Volume Data}, url = {http://dx.doi.org/10.1109/MCG.2009.104}, volume = {99}, year = {5555} }

TY - JOUR ID - citeulike:5811327 L3 - citeulike-article-id:5811327 N2 - In many areas of science and engineering, the desires to study a problem at the highest possible resolution have led to an explosive growth of data. It is imperative to reduce the data to a manageable scale for analysis and visualization. For high-precision floating-point data, compressing the data solely based on values can only achieve a limited saving. Further reduction is possible with the fact that usually only a smaller subset of the data is of interest in analysis. In this paper, we present an application-driven approach to compressing large-scale time-varying volume data. Our method 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. We show that our method achieves high compression rates and interactive rendering while preserving fine details surrounding regions of interest. Such an application-driven approach points us to a promising direction for coping with the large data problems facing computational scientists. IS - 1 JF - IEEE Computer Graphics and Applications SN - 0272-1716 AD - Los Alamitos, CA, USA TI - Application-Driven Compression for Visualizing Large-Scale Time-Varying Volume Data PB - IEEE Computer Society VL - 99 KW - visualisation-3d KW - visualisation-algorithms KW - visualisation-scientific AU - Wang, Chaoli AU - Yu, Hongfeng AU - Ma, Kwan PY - 5555/// UR - http://dx.doi.org/10.1109/MCG.2009.104 ER -