TY - GEN
T1 - Interpolation of scientific image databases
AU - Kinner, Eric Georg
AU - Lukasczyk, Jonas
AU - Rogers, David Honegger
AU - Maciejewski, Ross
AU - Garth, Christoph
N1 - Publisher Copyright:
© Eric Georg Kinner, Jonas Lukasczyk, David Honegger Rogers, Ross Maciejewski, and Christoph Garth.
PY - 2021/4/1
Y1 - 2021/4/1
N2 - This paper explores how recent convolutional neural network (CNN)-based techniques can be used to interpolate images inside scientific image databases. These databases are frequently used for the interactive visualization of large-scale simulations, where images correspond to samples of the parameter space (e.g., timesteps, isovalues, thresholds, etc.) and the visualization space (e.g., camera locations, clipping planes, etc.). These databases can be browsed post hoc along the sampling axis to emulate real-time interaction with large-scale datasets. However, the resulting databases are limited to their contained images, i.e., the sampling points. In this paper, we explore how efficiently and accurately CNN-based techniques can derive new images by interpolating database elements. We demonstrate on several real-world examples that the size of databases can be further reduced by dropping samples that can be interpolated post hoc with an acceptable error, which we measure qualitatively and quantitatively.
AB - This paper explores how recent convolutional neural network (CNN)-based techniques can be used to interpolate images inside scientific image databases. These databases are frequently used for the interactive visualization of large-scale simulations, where images correspond to samples of the parameter space (e.g., timesteps, isovalues, thresholds, etc.) and the visualization space (e.g., camera locations, clipping planes, etc.). These databases can be browsed post hoc along the sampling axis to emulate real-time interaction with large-scale datasets. However, the resulting databases are limited to their contained images, i.e., the sampling points. In this paper, we explore how efficiently and accurately CNN-based techniques can derive new images by interpolating database elements. We demonstrate on several real-world examples that the size of databases can be further reduced by dropping samples that can be interpolated post hoc with an acceptable error, which we measure qualitatively and quantitatively.
KW - Cinema database
KW - Image database
KW - Image interpolation
UR - http://www.scopus.com/inward/record.url?scp=85106948945&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85106948945&partnerID=8YFLogxK
U2 - 10.4230/OASIcs.iPMVM.2020.19
DO - 10.4230/OASIcs.iPMVM.2020.19
M3 - Conference contribution
AN - SCOPUS:85106948945
T3 - OpenAccess Series in Informatics
BT - 2nd International Conference of the DFG International Research Training Group 2057 - Physical Modeling for Virtual Manufacturing, iPMVM 2020
A2 - Garth, Christoph
A2 - Aurich, Jan C.
A2 - Linke, Barbara
A2 - Muller, Ralf
A2 - Ravani, Bahram
A2 - Weber, Gunther H.
A2 - Kirsch, Benjamin
PB - Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing
T2 - 2nd International Conference of the DFG International Research Training Group 2057 - Physical Modeling for Virtual Manufacturing, iPMVM 2020
Y2 - 16 November 2020 through 18 November 2020
ER -