论文标题
模型：用于学习视频编码的模式选择网络
ModeNet: Mode Selection Network For Learned Video Coding
论文作者
论文摘要
在本文中，提出了模式选择网络（模型），以增强基于深度学习的视频压缩。受传统视频编码的启发，模型的目的是使几种编码模式之间的竞争。所提出的模型学习并传达了框架的像素划分，用于将每个像素分配给最合适的编码模式。与不同的编码模式一起训练Modenet，以最大程度地降低利率延伸成本。它是一个灵活的组件，可以推广到其他系统，以允许不同的编码工具之间的竞争。在P框架编码任务上研究了Mod-Enet兴趣，该任务用于设计用于框架预测的框架的方法。基于模材的系统在学习的图像压缩2020（CLIC20）p框架编码轨道条件下评估时，实现了令人信服的性能。
In this paper, a mode selection network (ModeNet) is proposed to enhance deep learning-based video compression. Inspired by traditional video coding, ModeNet purpose is to enable competition among several coding modes. The proposed ModeNet learns and conveys a pixel-wise partitioning of the frame, used to assign each pixel to the most suited coding mode. ModeNet is trained alongside the different coding modes to minimize a rate-distortion cost. It is a flexible component which can be generalized to other systems to allow competition between different coding tools. Mod-eNet interest is studied on a P-frame coding task, where it is used to design a method for coding a frame given its prediction. ModeNet-based systems achieve compelling performance when evaluated under the Challenge on Learned Image Compression 2020 (CLIC20) P-frame coding track conditions.
