用机器学习加速视频编码的分块决策，降低计算复杂度。
Complexity Reduction Study Based on RD Costs Approximation for VVC Intra Partitioning
- 基于回归和强化学习预测编码单元的率失真代价
- 通过邻块信息与多尺度决策轨迹提升预测精度
- 适合需要高速编码的实时视频应用
本文针对通用视频编码（VVC）的帧内分块过程中的率失真优化（RDO）穷举搜索问题，开展复杂度降低研究。提出两种基于机器学习的方法：第一种为回归模型，预测给定编码单元（CU）的归一化率失真代价；第二种基于深度Q网络（DQN）的强化学习代理，从两个深度层级的CU决策轨迹中学习策略。两种方法均不依赖块大小，且利用邻近块的率失真信息作为输入特征。最后通过预设阈值筛选当前CU的合适分割方案，实现高效决策。该方法可有效减少编码时间开销。
原文摘要 · Abstract (English)
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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