Paper Info
Title
Towards PDE-Based Video Compression with Optimal Masks and Optic Flow.
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 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.
Year
DOI
Venue
2019
10.1007/978-3-030-22368-7_7
Lecture Notes in Computer Science
Keywords
Field
DocType
Partial differential equations,Inpainting,Laplace interpolation,Optic flow,Video reconstruction
Computer vision,Video reconstruction,Computer science,Flow (psychology),Inpainting,Artificial intelligence,Lossy image compression,Data compression,Partial differential equation
Conference
Volume
ISSN
Citations 
11603
0302-9743
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Laurent Hoeltgen100.34
Michael Breuß216825.45
Georg Radow300.34