RepCaM: Re-parameterization Content-aware Modulation for Neural Video Delivery

Recently, content-aware methods have been utilized to reduce the bandwidth and improve the quality of Internet video delivery. Existing methods train corresponding content-aware super-resolution (SR) models for each video chunk on the server and stream low-resolution (LR) video chunks along with SR models to the client. Previous works introduce additional partial parameters to privatize the models of different video chunks. However, this still leads to the accumulation of parameters and even fails to modulate when the length of video increases, bringing extra delivery costs and performance degradation. In this paper, we introduce a novel Re-parameterization Content-aware Modulation (RepCaM) method to modulate all the video chunks with an end-to-end training strategy. Our method adopts extra parallel-cascade parameters during training to fit multiple chunks while removing the additional parameters through re-parameterization during inference. Therefore, RepCaM increases no extra model size compared with the original SR model. Moreover, in order to improve the training efficiency on servers, we propose an online Video Patch Sampling (VPS) method to speed up the training convergence. We conduct extensive experiments on VSD4K and newly collected dataset (VSD4K-2022), achieving state-of-the-art results in video restoration quality and delivery bandwidth compression. Code is available at: https://github.com/Neural-video-delivery/RepCaM-Pytorch-NOSSDAV2023.