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

arXiv: https://arxiv.org/abs/2511.20349 · HTML: https://mlsanity.com/paper/2511.20349/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 11 references that are in the map.
- First posted on arXiv 25 Nov 2025.

## Builds on (most cited first, 3 of 3)

- [BVI-DVC: A Training Database for Deep Video Compression](https://mlsanity.com/paper/2003.13552/) (2020)
- [DeepQTMT: A Deep Learning Approach for Fast QTMT-based CU Partition of Intra-mode VVC](https://mlsanity.com/paper/2006.13125/) (2020)
- [Machine Learning based Efficient QT-MTT Partitioning Scheme for VVC Intra Encoders](https://mlsanity.com/paper/2103.05319/) (2021)

## Abstract (arXiv)

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.