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.

中文翻译：

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ModeNet：用于学习视频编码的模式选择网络

在本文中，提出了一种模式选择网络（ModeNet）来增强基于深度学习的视频压缩。受传统视频编码的启发，ModeNet 的目的是实现多种编码模式之间的竞争。提议的 ModeNet 学习并传达帧的逐像素分区，用于将每个像素分配给最合适的编码模式。ModeNet 与不同的编码模式一起训练，以最小化率失真成本。它是一个灵活的组件，可以推广到其他系统以允许不同编码工具之间的竞争。Mod-eNet 兴趣是在 P 帧编码任务上研究的，它用于设计一种给定预测的帧编码方法。