Fast Machine Learning Aided Intra Mode Decision for Real-Time VVC Intra Coding

Abstract

Reducing the huge computational complexity of intra mode decision is the key to real-time Video Coding (VVC). This paper proposes a fast intra mode decision scheme that takes advantage of lightweight machine learning (ML) models to classify intra modes into fifteen clusters. The cluster is further refined using one of the three proposed strategies to select the most optimal mode. Our experimental results with the fastest configuration of the practical uvg266 encoder show that the proposed methods yield a competitive rate-distortion-complexity trade-off over a conventional rough mode decision (RMD). To the best of our knowledge, this is the first work to successfully reduce the complexity of RMD in a practical VVC encoder with the use of ML techniques.