Complexity Reduction Study Based on RD Costs Approximation for VVC Intra Partitioning

M. E. A. Kherchouche, Franck Galpin, Thierry Dumas, F. Schnitzler, Daniel Ménard, L. Zhang

Abstract: 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).

External IDs:dblp:conf/dcc/KherchoucheGDSM25