APSIPA Transactions on Signal and Information Processing > Vol 8 > Issue 1

Extended multiple feature-based classifications for adaptive loop filtering

Johannes Erfurt, Fraunhofer Heinrich Hertz Institute (HHI), Germany, johannes.erfurt@hhi.fraunhofer.de , Wang-Q Lim, Fraunhofer Heinrich Hertz Institute (HHI), Germany, Heiko Schwarz, Fraunhofer Heinrich Hertz Institute (HHI), Germany, Detlev Marpe, Fraunhofer Heinrich Hertz Institute (HHI), Germany, Thomas Wiegand, Fraunhofer Heinrich Hertz Institute (HHI), Germany
 
Suggested Citation
Johannes Erfurt, Wang-Q Lim, Heiko Schwarz, Detlev Marpe and Thomas Wiegand (2019), "Extended multiple feature-based classifications for adaptive loop filtering", APSIPA Transactions on Signal and Information Processing: Vol. 8: No. 1, e28. http://dx.doi.org/10.1017/ATSIP.2019.19

Publication Date: 14 Nov 2019
© 2019 Johannes Erfurt, Wang-Q Lim, Heiko Schwarz, Detlev Marpe and Thomas Wiegand
 
Subjects
 
Keywords
Video compressionAdaptive loop filteringMultiple classificationsSAO filtering
 

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This is published under the terms of the Creative Commons Attribution licence.

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In this article:
1. INTRODUCTION 
2. REVIEW OF ALF 
3. MULTIPLE FEATURE-BASED CLASSIFICATIONS 
4. CLASSIFICATIONS WITH CONFIDENCE LEVEL 
5. EXTENDED MCALF 
6. SIMULATION RESULTS AND ANALYSIS 
7. CONCLUSION 

Abstract

Recent progress in video compression is seemingly reaching its limits making it very hard to improve coding efficiency significantly further. The adaptive loop filter (ALF) has been a topic of interest for many years. ALF reaches high coding gains and has motivated many researchers over the past years to further improve the state-of-the-art algorithms. The main idea of ALF is to apply a classification to partition the set of all sample locations into multiple classes. After that, Wiener filters are calculated and applied for each class. Therefore, the performance of ALF essentially relies on how its classification behaves. In this paper, we extensively analyze multiple feature-based classifications for ALF (MCALF) and extend the original MCALF by incorporating sample adaptive offset filtering. Furthermore, we derive new block-based classifications which can be applied in MCALF to reduce its complexity. Experimental results show that our extended MCALF can further improve compression efficiency compared to the original MCALF algorithm.

DOI:10.1017/ATSIP.2019.19