Complexity Reduction Study Based on RD Costs Approximation for VVC Intra Partitioning
This work addresses efficiency improvements for video codec developers, but it is incremental as it builds on existing methods with size-independent features.
The paper tackles the high computational complexity of exhaustive search in VVC intra partitioning by proposing two machine learning techniques that predict RD costs and use RL for decision-making, achieving complexity reduction with reported gains.
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.