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Interpolation of Scientific Image Databases

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

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Author Details

Eric Georg Kinner
  • Technische Universität Kaiserslautern, Germany
Jonas Lukasczyk
  • Arizona State University, Tempe, AZ, US
David Honegger Rogers
  • Los Alamos Research Laboratory, NM, US
Ross Maciejewski
  • Arizona State University, Tempe, AZ, US
Christoph Garth
  • Technische Universität Kaiserslautern, Germany

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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)
https://doi.org/10.4230/OASIcs.iPMVM.2020.19

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.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Image processing
Keywords
  • Image Interpolation
  • Image Database
  • Cinema Database

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References

  1. V. Adhinarayanan, W. c. Feng, D. Rogers, J. Ahrens, and S. Pakin. Characterizing and Modeling Power and Energy for Extreme-Scale In-Situ Visualization. In 2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS), pages 978-987, May 2017. URL: https://doi.org/10.1109/IPDPS.2017.113.
  2. James Ahrens, Sébastien Jourdain, Patrick O'Leary, John Patchett, David H. Rogers, and Mark Petersen. An Image-based Approach to Extreme Scale in Situ Visualization and Analysis. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC '14, pages 424-434, Piscataway, NJ, USA, 2014. IEEE Press. URL: https://doi.org/10.1109/SC.2014.40.
  3. Simon Baker, Daniel Scharstein, J. P. Lewis, Stefan Roth, Michael J. Black, and Richard Szeliski. A database and evaluation methodology for optical flow. International Journal of Computer Vision, 92(1):1-31, March 2011. https://vision.middlebury.edu/flow/eval/. URL: https://doi.org/10.1007/s11263-010-0390-2.
  4. Wenbo Bao, Wei-Sheng Lai, Chao Ma, Xiaoyun Zhang, Zhiyong Gao, and Ming-Hsuan Yang. Depth-Aware Video Frame Interpolation. CoRR, abs/1904.00830, 2019. Implementation from https://github.com/baowenbo/DAIN. URL: http://arxiv.org/abs/1904.00830.
  5. Anne S. Berres, Terece L. Turton, Mark Petersen, David H. Rogers, and James P. Ahrens. Video Compression for Ocean Simulation Image Databases. In Karsten Rink, Ariane Middel, Dirk Zeckzer, and Roxana Bujack, editors, Workshop on Visualisation in Environmental Sciences (EnvirVis). The Eurographics Association, 2017. URL: https://doi.org/10.2312/envirvis.20171104.
  6. Martin Burtscher and Paruj Ratanaworabhan. FPC: A High-Speed Compressor for Double-Precision Floating-Point Data. Computers, IEEE Transactions on, 58:18-31, February 2009. URL: https://doi.org/10.1109/TC.2008.131.
  7. S. Di and F. Cappello. Fast Error-Bounded Lossy HPC Data Compression with SZ. In 2016 IEEE International Parallel and Distributed Processing Symposium (IPDPS), pages 730-739, 2016. Google Scholar
  8. O. Fernandes, S. Frey, F. Sadlo, and T. Ertl. Space-time volumetric depth images for in-situ visualization. In 2014 IEEE 4th Symposium on Large Data Analysis and Visualization (LDAV), pages 59-65, 2014. Google Scholar
  9. IEEEVIS. Scientific Visualization Contest. http://www.uni-kl.de/sciviscontest/, 2016. Google Scholar
  10. Timothy Jeruzalski, John Kanji, Alec Jacobson, and David I.W. Levin. Collision-Aware and Online Compression of Rigid Body Simulations via Integrated Error Minimization. Computer Graphics Forum (Proc. SCA), 2018. Google Scholar
  11. Pavol Klacansky. Open SciVis Datasets, December 2017. https://klacansky .com/open-scivis-datasets/. URL: https://klacansky.com/open-scivis-datasets/.
  12. Anand Kumar, Xingquan Zhu, Yi-Cheng Tu, and Sagar Pandit. Compression in molecular simulation datasets. In Changyin Sun, Fang Fang, Zhi-Hua Zhou, Wankou Yang, and Zhi-Yong Liu, editors, Intelligence Science and Big Data Engineering, pages 22-29, Berlin, Heidelberg, 2013. Springer Berlin Heidelberg. Google Scholar
  13. Julian Kunkel, Michael Kuhn, and Thomas Ludwig. Exascale Storage Systems - An Analytical Study of Expenses. Supercomputing Frontiers and Innovations, 1(1), 2014. URL: https://superfri.org/superfri/article/view/20.
  14. Sriram Lakshminarasimhan, Neil Shah, Stephane Ethier, Seung-Hoe Ku, C. S. Chang, Scott Klasky, Rob Latham, Rob Ross, and Nagiza F. Samatova. ISABELA for effective in situ compression of scientific data. Concurrency and Computation: Practice and Experience, 25(4):524-540, 2013. URL: https://doi.org/10.1002/cpe.2887.
  15. Juelin Leng, Guoliang Xu, and Yongjie Zhang. Medical image interpolation based on multi-resolution registration. Computers + Mathematics with Applications, 66(1):1-18, 2013. URL: https://doi.org/10.1016/j.camwa.2013.04.026.
  16. H. Li, Y. Yuan, and Q. Wang. Video Frame Interpolation Via Residue Refinement. In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 2613-2617, 2020. Google Scholar
  17. J. Y. Lin, T. Liu, E. C. Wu, and C. . J. Kuo. A fusion-based video quality assessment (fvqa) index. In Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific, pages 1-5, 2014. Google Scholar
  18. Yu-Lun Liu, Yi-Tung Liao, Yen-Yu Lin, and Yung-Yu Chuang. Deep Video Frame Interpolation using Cyclic Frame Generation. In Proceedings of the 33rd Conference on Artificial Intelligence (AAAI), 2019. Implementation from https://github.com/alex04072000/CyclicGen. Google Scholar
  19. Ziwei Liu, Raymond Yeh, Xiaoou Tang, Yiming Liu, and Aseem Agarwala. Video Frame Synthesis using Deep Voxel Flow. In Proceedings of International Conference on Computer Vision (ICCV), October 2017. Implementation from https://github.com/liuziwei7/voxel-flow and https://github.com/lxx1991/pytorch-voxel-flow. Google Scholar
  20. J. Lukasczyk, E. Kinner, J. Ahrens, H. Leitte, and C. Garth. VOIDGA: A View-Approximation Oriented Image Database Generation Approach. In 2018 IEEE 8th Symposium on Large Data Analysis and Visualization (LDAV), pages 12-22, 2018. Google Scholar
  21. Jonas Lukasczyk, Gunther Weber, Ross Maciejewski, Christoph Garth, and Heike Leitte. Nested Tracking Graphs. Comput. Graph. Forum, 36(3):12–22, 2017. URL: https://doi.org/10.1111/cgf.13164.
  22. Zarija Lukic. Nyx Cosmological Simulation Data. http://dx.doi.org /10.21227/k8gb-vq78, 2019. URL: https://doi.org/10.21227/k8gb-vq78.
  23. Dhruv Mahajan, Fu-Chung Huang, Wojciech Matusik, Ravi Ramamoorthi, and Peter Belhumeur. Moving Gradients: A Path-Based Method for Plausible Image Interpolation. ACM Trans. Graph., 28(3), July 2009. URL: https://doi.org/10.1145/1531326.1531348.
  24. Bill Lorensen Marc Levoy, Graham Gash. University of North Carolina Volume Rendering Test Data Set, 1987. URL: https://graphics.stanford.edu/data/voldata/.
  25. S. Meyer, O. Wang, H. Zimmer, M. Grosse, and A. Sorkine-Hornung. Phase-based frame interpolation for video. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1410-1418, June 2015. Implementation from https:// github.com/justanhduc/Phase-based-Frame-Interpolation. URL: https://doi.org/10.1109/CVPR.2015.7298747.
  26. MPAS. Model for Prediction Across Scales. https://mpas-dev.github.io/, 2019. Google Scholar
  27. Simon Niklaus, Long Mai, and Feng Liu. Video Frame Interpolation via Adaptive Separable Convolution. CoRR, abs/1708.01692, 2017. Implementation from https://github.com/sniklaus/sepconv-slomo. URL: http://arxiv.org/abs/1708.01692.
  28. John Patchett and Galen Gisler. Deep Water Impact Ensemble Data Set. Technical report, LANL, 2017. LA-UR-17-21595. URL: https://datascience.dsscale.org/wp-content/uploads/2017/03/DeepWaterImpactEnsembleDataSet.pdf.
  29. PNG. Portable Network Graphics Specification. https://tools.ietf.org/html /rfc2083, March 1997. Google Scholar
  30. H. R. Sheikh and A. C. Bovik. Image information and visual quality. IEEE Transactions on Image Processing, 15(2):430-444, 2006. Google Scholar
  31. D. Tao, S. Di, Z. Chen, and F. Cappello. Significantly Improving Lossy Compression for Scientific Data Sets Based on Multidimensional Prediction and Error-Controlled Quantization. In 2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS), pages 1129-1139, 2017. Google Scholar
  32. Julien Tierny, Guillaume Favelier, Joshua A. Levine, Charles Gueunet, and Michael Michaux. The Topology ToolKit. CoRR, abs/1805.09110, 2018. URL: http://arxiv.org/abs/1805.09110.
  33. Z. Wang, E. P. Simoncelli, and A. C. Bovik. Multiscale structural similarity for image quality assessment. In The Thrity-Seventh Asilomar Conference on Signals, Systems Computers, 2003, volume 2, pages 1398-1402 Vol.2, 2003. Google Scholar
  34. Zhou Wang and A. C. Bovik. A universal image quality index. IEEE Signal Processing Letters, 9(3):81-84, 2002. Google Scholar
  35. Zhou Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4):600-612, 2004. Google Scholar
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