Deep Adaptive Quantization for Practical Video Compression

Shuai Huo, Hewei Liu, Jiawen Gu, Dengchao Jin, Meng Lei, Bo Huang, Chao Zhou

Abstract: In this work, we propose a deep learning-based adaptive quantization method to promote video coding performance. Due to inter-prediction and reference mechanism, the block-level quantization parameter (QP) not only influences current block distortion but also has complex temporal propagation effects on subsequent coding frames. Our idea is to utilize a deep network to model the complex temporal propagation relationship of quantization. As shown in Fig. 1, the deep network directly predicts all block-level QPs of the frame for the traditional encoder without changing the standard decoder. Since our network deploys only on the encoder side and has low inference complexity, it can be easily applied in practice. In addition, we use a learned coding network as a proxy of the traditional codec to train our network.

External IDs:dblp:conf/dcc/HuoLGJ0HZ25