GM-RF: An AV1 Intra-Frame Fast Decision Based on Random Forest

Abstract

This paper presents the Grouping of Modes based on Random Forest (GM-RF), a fast decision algorithm for the AOMedia Video 1 (AV1) intra-frame prediction applying machine learning (ML). AV1 implements a wide variety of intra-frame prediction tools, significantly increasing the required computational effort. The GM-RF uses trained Random Forest (RF) models to reduce the number of intra-frame prediction modes evaluated for each encoded block. Experimental results show that the GM-RF achieves an average time savings of 50.19%, with a BD-BR of 7.41%. Compared with related works, GM-RF reached time savings from 5.6 to 10 times higher at a cost of a higher BDBR. To the best of the authors' knowledge, this is the first solution in the literature using ML to reduce the AV1 intra-frame prediction computational effort.