Two-Step Predictive Models for Low-Complexity Multiple Transform Selection in VVC - Camargo, Caroline; Palomino, Daniel; Correa, Guilherme (Anais do Seminário Integrado de Software e Hardware (SEMISH), 2026)
The Versatile Video Coding (VVC) standard achieves high compression efficiency but with significantly increased computational complexity. A major contributor is the Multiple Transform Selection (MTS) process, which exhaustively evaluates multiple transform combinations. This work proposes a machine learning approach using lightweight decision trees to replace the exhaustive MTS search. The models are trained with balanced sampling, feature selection, and hyperparameter optimization. Experiments in the All Intra configuration show an average encoding time reduction of 18.76% with only a 0.67% increase in BD-rate, reaching up to 30.90% time savings in the transform stage.