@article{BHR2020,
 pages         = {144--156},
 author        = {Breuß, Michael and Hoeltgen, Laurent and Radow, Georg},
 journaltitle  = {Journal of Mathematical Imaging and Vision},
 journal       = {Journal of Mathematical Imaging and Vision},
 title         = {Towards {PDE}-Based Video Compression with Optimal Masks Prolongated by Optic Flow},
 publisher     = {Springer Science and Business Media {LLC}},
 number        = {2},
 doi           = {10.1007/s10851-020-00973-6},
 keywords      = {reviewed},
 owner         = {laurent},
 issn          = {0924-9907},
 year          = {2020},
 date          = {2020},
 volume        = {63},
 abstract      = {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 an optimal selection of data. For the possible extension to video compression, this 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. In this way, 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. In doing this, we discuss in detail the influence of possible computational setups. We show that the approach preserves a reasonable quality in the reconstruction and is very robust against errors in the flow field.},
 timestamp     = {2020.07.15}
}