A signal adaptive diffusion filter for video coding: Mathematical framework and complexity reductions

Autor: Michael Schafer, Detlev Marpe, Henkel Anastasia, Jonathan Pfaff, Jennifer Rasch, Thomas Wiegand, Heiko Schwarz
Přispěvatelé: Publica
Rok vydání: 2020
Předmět:
Zdroj: Signal Processing: Image Communication. 85:115861
ISSN: 0923-5965
DOI: 10.1016/j.image.2020.115861
Popis: In this paper we combine video compression and modern image processing methods. We construct novel iterative filter methods for prediction signals based on Partial Differential Equation (PDE) based methods. The mathematical framework of the employed diffusion filter class is given and some desirable properties are stated. In particular, two types of diffusion filters are constructed: a uniform diffusion filter using a fixed filter mask and a signal adaptive diffusion filter that incorporates the structures of the underlying prediction signal. The latter has the advantage of not attenuating existing edges while the uniform filter is less complex. The filters are embedded into a software based on HEVC with additional QTBT (Quadtree plus Binary Tree) and MTT (Multi-Type-Tree) block structure. In this setting, several measures to reduce the coding complexity of the tool are introduced, discussed and tested thoroughly. The coding complexity is reduced by up to 70% while maintaining over 80% of the gain. Overall, the diffusion filter method achieves average bitrate savings of 2.27% for Random Access having an average encoder runtime complexity of 119% and 117% decoder runtime complexity. For individual test sequences, results of 7.36% for Random Access are accomplished.
Databáze: OpenAIRE
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