In this paper, a complexity study is conducted for Versatile Video Codec (VVC) intra partitioning to accelerate the exhaustive search involved in Rate-Distortion Optimization (RDO) process. To address this problem, two main machine learning techniques are proposed and compared. Unlike existing methods, the proposed approaches are size independent and incorporate the Rate-Distortion (RD) costs of neighboring blocks as input features. The first method is a regression based technique that predicts normalized RD costs of a given Coding Unit (CU). As partitioning possesses the Markov property, the associated decision-making problem can be modeled as a Markov Decision Process (MDP) and solved by Reinforcement Learning (RL). The second approach is a RL agent learned from trajectories of CU decision across two depths with Deep Q-Network (DQN) algorithm. Then a pre-determined thresholds are applied for both methods to select a suitable split for the current CU.
@article{arxiv.2511.20349,
title = {Complexity Reduction Study Based on RD Costs Approximation for VVC Intra Partitioning},
author = {M. E. A. Kherchouche and F. Galpin and T. Dumas and F. Schnitzler and D. Menard and L. Zhang},
journal= {arXiv preprint arXiv:2511.20349},
year = {2025}
}