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Towards PDE-Based Video Compression with Optimal Masks and Optic Flow

  • Lossy image compression methods based on partial differential equations have received much attention in recent years. They may yield high quality results but rely on the computationally expensive task of finding optimal data. For the possible extension to video compression, the data selection is a crucial issue. In this context one could either analyse the video sequence as a whole or perform a frame-by-frame optimisation strategy. Both approaches are prohibitive in terms of memory and run time. In this work we propose to restrict the expensive computation of optimal data to a single frame and to approximate the optimal reconstruction data for the remaining frames by prolongating it by means of an optic flow field. We achieve a notable decrease in the computational complexity. As a proof-of-concept, we evaluate the proposed approach for multiple sequences with different characteristics. We show that the method preserves a reasonable quality in the reconstruction, and is very robust against errors in the flow field.

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Metadaten
Author: Laurent HoeltgenORCiD, Michael BreußGND, Georg RadowGND
DOI:https://doi.org/10.1007/978-3-030-22368-7_7
ISBN:978-3-030-22367-0
ISBN:978-3-030-22368-7
Title of the source (English):Scale Space and Variational Methods in Computer Vision : 7th International Conference, SSVM 2019, Hofgeismar, Germany, June 30 – July 4, 2019, Proceedings
Publisher:Springer
Place of publication:Cham
Editor: Jan Lellmann, Martin Burger, Jan Modersitzki
Document Type:Conference publication peer-reviewed
Language:English
Year of publication:2019
Tag:Partial differential equations Inpainting Laplace interpolation Optic flow Video reconstruction
First Page:79
Last Page:91
Series ; volume number:Lecture Notes in Computer Science book series ; volume 11603
Faculty/Chair:Fakultät 1 MINT - Mathematik, Informatik, Physik, Elektro- und Informationstechnik / FG Angewandte Mathematik
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