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GPU Accelerated Sparse Representation of Light Fields
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. (Immersiv visualisering)ORCID iD: 0000-0003-2113-0122
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-7765-1747
2019 (English)In: VISIGRAPP - 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Prague, Czech Republic, February 25-27, 2019., 2019, Vol. 4, p. 177-182Conference paper, Published paper (Refereed)
Abstract [en]

We present a method for GPU accelerated compression of light fields. The approach is by using a dictionary learning framework for compression of light field images. The large amount of data storage by capturing light fields is a challenge to compress and we seek to accelerate the encoding routine by GPGPU computations. We compress the data by projecting each data point onto a set of trained multi-dimensional dictionaries and seek the most sparse representation with the least error. This is done by a parallelization of the tensor-matrix product computed on the GPU. An optimized greedy algorithm to suit computations on the GPU is also presented. The encoding of the data is done segmentally in parallel for a faster computation speed while maintaining the quality. The results shows an order of magnitude faster encoding time compared to the results in the same research field. We conclude that there are further improvements to increase the speed, and thus it is not too far from an interacti ve compression speed.

Place, publisher, year, edition, pages
2019. Vol. 4, p. 177-182
Keywords [en]
Light Field Compression, Gpgpu Computation, Sparse Representation
National Category
Media and Communication Technology
Identifiers
URN: urn:nbn:se:liu:diva-157009DOI: 10.5220/0007393101770182ISBN: 978-989-758-354-4 (print)OAI: oai:DiVA.org:liu-157009DiVA, id: diva2:1317301
Conference
VISAPP - 14th International Conference on Computer Vision Theory and Applications, Prague, Czech Republic, February 25-27, 2019.
Available from: 2019-05-22 Created: 2019-05-22 Last updated: 2019-06-14Bibliographically approved

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Baravdish, GabrielMiandji, EhsanUnger, Jonas

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf