Fast Inter-Prediction based on Decision Trees for AV1 encoding

TL;DR

This paper proposes a decision tree-based method to selectively test inter-prediction modes in AV1 encoding, significantly reducing encoding time while maintaining compression efficiency.

Contribution

It introduces a novel decision tree approach to optimize AV1 encoding by reducing unnecessary mode testing, improving speed without sacrificing quality.

Findings

Average encoding time reduced by 43.4%.

Limited impact on coding efficiency.

Effective feature extraction for decision-making.

Abstract

The AOMedia Video 1 (AV1) standard can achieve considerable compression efficiency thanks to the usage of many advanced tools and improvements, such as advanced inter-prediction modes. However, these come at the cost of high computational complexity of encoder, which may limit the benefits of the standard in practical applications. This paper shows that not all sequences benefit from using all such modes, which indicates that a number of encoder optimisations can be introduced to speed up AV1 encoding. A method based on decision trees is proposed to selectively decide whether to test all inter modes. Appropriate features are extracted and used to perform the decision for each block. Experimental results show that the proposed method can reduce the encoding time on average by 43.4\% with limited impact on the coding efficiency.