论文标题
基于学习的视频编码的光流和模式选择
Optical Flow and Mode Selection for Learning-based Video Coding
论文作者
论文摘要
本文介绍了一种基于两个互补自动编码器的新方法，用于框架间编码：MOFNET和CODECNET。 MOFNET旨在计算和传达光流和像素编码模式的选择。光流用于执行帧对代码的预测。编码模式选择可以在通过编解码器直接复制预测或传输之间的竞争。在学习的图像压缩2020（CLIC20）P框架编码条件下，在挑战中评估了所提出的编码方案，在该条件下，该条件与最先进的视频编解码器ITU/MPEG HEVC相同。此外，复制预测的可能性使得能够以端到端的方式学习光流，即不依赖培训和/或专用损失项。
This paper introduces a new method for inter-frame coding based on two complementary autoencoders: MOFNet and CodecNet. MOFNet aims at computing and conveying the Optical Flow and a pixel-wise coding Mode selection. The optical flow is used to perform a prediction of the frame to code. The coding mode selection enables competition between direct copy of the prediction or transmission through CodecNet. The proposed coding scheme is assessed under the Challenge on Learned Image Compression 2020 (CLIC20) P-frame coding conditions, where it is shown to perform on par with the state-of-the-art video codec ITU/MPEG HEVC. Moreover, the possibility of copying the prediction enables to learn the optical flow in an end-to-end fashion i.e. without relying on pre-training and/or a dedicated loss term.
