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

• URL: http://arxiv.org/abs/2511.20349v1
• Date: Tue, 25 Nov 2025 14:25:57 GMT
• Title: Complexity Reduction Study Based on RD Costs Approximation for VVC Intra Partitioning
• Abstract summary: Two main machine learning techniques are proposed and compared.<n>The proposed approaches are size independent and incorporate the Rate-Distortion (RD) costs of neighboring blocks as input features.
• Score: 1.190427345303959
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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