Deep Space-Time Video Upsampling Networks

Jaeyeon Kang, Younghyun Jo, Seoung Wug Oh, Peter Vajda, Seon Joo Kim

Abstract: Video super-resolution (VSR) and frame interpolation (FI) are traditional computer vision problems, and the performance have been improving by incorporating deep learning recently. In this paper, we investigate the problem of jointly upsampling videos both in space and time, which is becoming more important with advances in display systems. One solution for this is to run VSR and FI, one by one, independently. This is highly inecient as heavy deep neural networks (DNN) are involved in each solution. To this end, we propose an end- to-end DNN framework for the space-time video upsampling by effciently merging VSR and FI into a joint framework. In our framework, a novel weighting scheme is proposed to fuse all input frames effectively without explicit motion compensation for efficient processing of videos. The results show better results both quantitatively and qualitatively, while reducing the computation time (x7 faster) and the number of parameters (30%) compared to baselines. Our source code is available at https://github.com/JaeYeonKang/STVUN-Pytorch.