Linear Multivariate Decision Trees for Fast QTMT Partitioning in VVC

Abstract

The demand for ultra-high definition (UHD) content has led to the development of advanced compression tools to enhance the efficiency of standard codecs. One such tool is the Quaternary Tree and Multi-Type Tree (QTMT) used in the Versatile Video Coding (VVC), which significantly improves coding efficiency over previous standards, but introduces substantially higher computational complexity. To address the challenge of reducing computational complexity with minimal impact on coding efficiency, this paper presents a novel approach for intra-coding 360$^{\circ }$ video in Equirectangular Projection (ERP) format. By exploiting distinct complexity and spatial characteristics of the North, Equator, and South regions in ERP images, the proposed method is devised upon a region-based approach, using novel linear multivariate decision trees to determine whether a given partition type can be skipped. Optimisation of model parameters and an adaptive thresholding method is also presented. The experimental results show a Complexity Gain of approximately 16% with a negligible BD-Rate loss of only 0.06%, surpassing current state-of-the-art methods in terms of complexity gain per percentage point of BD-Rate loss.