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DTSTART;TZID=America/New_York:20241118T100500
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UID:submissions.supercomputing.org_SC24_sess749_ws_drbsd104@linklings.com
SUMMARY:An Exploration of How Volume Rendering is Impacted by Lossy Data R
 eduction
DESCRIPTION:Yanni Etchi (Los Alamos National Laboratory (LANL), University
  of North Carolina at Chapel Hill); Daoce Wang (Indiana University, Los Al
 amos National Laboratory (LANL)); and Pascal Grosset, Terece Turton, James
  Ahrens, and David Rogers (Los Alamos National Laboratory (LANL))\n\nData 
 reduction is now frequently used by simulations to reduce the amount of da
 ta that needs to be stored. Consequently, several error-bound lossy data r
 eduction techniques have been developed to help compress scientific datase
 ts while trying to maximize quality. However, their impact on visualizatio
 n has hardly been studied and is not very well understood. In this paper, 
 we do an in-depth analysis of the impact of lossy data reduction on volume
  rendering to try to determine which parameters, such as characteristics o
 f datasets, opacity, color affect the perception quality of lossy data red
 uction.\n\nTag: Data Compression, Data Movement and Memory, Middleware and
  System Software\n\nRegistration Category: Workshop Reg Pass\n\nSession Ch
 airs: Sheng Di (Argonne National Laboratory (ANL), University of Chicago);
  Ana Gainaru (Oak Ridge National Laboratory (ORNL)); Sian Jin (Temple Univ
 ersity); Xin Liang (Oregon State University); Kento Sato (RIKEN Center for
  Computational Science (R-CCS)); and Dingwen Tao (Institute of Computing T
 echnology, Chinese Academy of Sciences; University of Chinese Academy of S
 ciences)\n\n
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