VVC achieves up to 50% bit-rate savings over HEVC at the cost of increased encoding complexity, largely due to the QTMTT partitioning structure. This work surveys the evolution of the VVC Test Model (VTM) and evaluates partitioning acceleration techniques considering changes in complexity and internal heuristics across VTM versions.

This paper presents a comparison study of two machine-learning techniques to accelerate the Versatile Video Coding (VVC) intra-partitioning process in QuadTree (QT) configuration: a regression model for predicting Rate-Distortion (RD) costs and a Deep Q-Network (DQN)-based Reinforcement Learning (RL) approach that models partitioning as a Markov Decision Process (MDP). Both methods are size-independent and utilize neighboring RD costs and threshold values to optimize the splits of Coding Units (CUs).