Machine Learning Accelerated Transform Search For AV1

Abstract

AV1 is the state-of-the-art open and royalty-free video compression format that achieves significant bitrate savings over previous generation of video codecs. One of AV1’s major improvement over its predecessor VP9 is the support of more diverse and flexible transform size and kernel selection. However, it also drastically increases the search space for transform unit rate-distortion optimization in AV1 encoders. Unlike conventional encoder speed features that are based on heuristics, we propose a machine learning (ML) based approach to accelerate the transform size and kernel search for AV1. The ML models use input features extracted from the prediction residue block such as standard deviation, correlation and energy distribution. The output of the models indicates the estimated likelihood of which transform size and kernel would be selected as the optimal choice. Based on the ML models, the encoder can prune out the transform size and kernel candidates that are unlikely to be selected and save unnecessary computation to compute their rate-distortion cost. The proposed approach is implemented and tested on the AV1 reference library libaom. The experimental results show that satisfactory encoding speed improvement can be achieved with extremely low compression performance loss. The framework and methodology can also be easily migrated to other video codecs and implementations.