Extended multiple feature-based classifications for adaptive loop filtering

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.