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.
@article{arxiv.2007.02532,
title = {ModeNet: Mode Selection Network For Learned Video Coding},
author = {Théo Ladune and Pierrick Philippe and Wassim Hamidouche and Lu Zhang and Olivier Déforges},
journal= {arXiv preprint arXiv:2007.02532},
year = {2020}
}