A Machine Learning Approach to Accurate Sequence-Level Rate Control Scheme for Video Coding
Yangfan Sun, Mouqing Jin, Li Li, Zhu Li · 2018
In this paper, we propose a two-pass encoding framework to handle the problem of sequence-level rate control. We consider the sequence-level encoding parameter constant rate factor (CRF) as the factor to be adjusted. The proposed framework mainly has two key contributions. First, we provide a second order model to characterize the relationship between the bitrate and CRF. The proposed second order model outperforms the traditional linear model significantly. Second, we adopt a shallow neural network to train the relationship between the content-dependent features with the second-order model parameters. The proposed neural network is quite simple but able to estimate the model parameters accurately. We implement the proposed algorithm under tensorflow. Experimental results show that our proposed method obviously outperforms the state-of-the-art method.