Linear Multivariate Decision Trees for Fast QTMT Partitioning in VVC
Filipe, J.
;
Távora, L.M.
;
Faria, S.M.M.
;
Navarro, A.
;
Assunção, P.A.
IEEE Open Journal of Signal Processing Vol. 6, Nº , pp. 175 - 183, , 2025.
ISSN (print): 2644-1322
ISSN (online): 2644-1322
Scimago Journal Ranking: 1,24 (in 2023)
Digital Object Identifier: 10.1109/OJSP.2025.3528897
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◦ 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.