2026-07-24T06:01:56Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:13768 2024-03-06T10:31:17Z ddc:004 open_access

Interpolation of Scientific Image Databases Kinner, Eric Georg Lukasczyk, Jonas Rogers, David Honegger Maciejewski, Ross Garth, Christoph Image Interpolation Image Database Cinema Database 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Eric Georg Kinner and Jonas Lukasczyk and David Honegger Rogers and Ross Maciejewski and Christoph Garth 2021 Is Part Of OASIcs, Volume 89, 2nd International Conference of the DFG International Research Training Group 2057 – Physical Modeling for Virtual Manufacturing (iPMVM 2020) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/OASIcs.iPMVM.2020.19 urn:nbn:de:0030-drops-137686 https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.iPMVM.2020.19 eng https://creativecommons.org/licenses/by/4.0/legalcode