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DOI: 10.4230/OASIcs.iPMVM.2020.19
Interpolation of Scientific Image Databases

Authors: Eric Georg Kinner, Jonas Lukasczyk, David Honegger Rogers, Ross Maciejewski, and Christoph Garth

Published in: OASIcs, Volume 89, 2nd International Conference of the DFG International Research Training Group 2057 – Physical Modeling for Virtual Manufacturing (iPMVM 2020)

Abstract
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.
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Eric Georg Kinner, Jonas Lukasczyk, David Honegger Rogers, Ross Maciejewski, and Christoph Garth. Interpolation of Scientific Image Databases. In 2nd International Conference of the DFG International Research Training Group 2057 – Physical Modeling for Virtual Manufacturing (iPMVM 2020). Open Access Series in Informatics (OASIcs), Volume 89, pp. 19:1-19:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{kinner_et_al:OASIcs.iPMVM.2020.19,
  author =	{Kinner, Eric Georg and Lukasczyk, Jonas and Rogers, David Honegger and Maciejewski, Ross and Garth, Christoph},
  title =	{{Interpolation of Scientific Image Databases}},
  booktitle =	{2nd International Conference of the DFG International Research Training Group 2057 – Physical Modeling for Virtual Manufacturing (iPMVM 2020)},
  pages =	{19:1--19:17},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-183-2},
  ISSN =	{2190-6807},
  year =	{2021},
  volume =	{89},
  editor =	{Garth, Christoph and Aurich, Jan C. and Linke, Barbara and M\"{u}ller, Ralf and Ravani, Bahram and Weber, Gunther H. and Kirsch, Benjamin},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.iPMVM.2020.19},
  URN =		{urn:nbn:de:0030-drops-137686},
  doi =		{10.4230/OASIcs.iPMVM.2020.19},
  annote =	{Keywords: Image Interpolation, Image Database, Cinema Database}
}
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