Neural Enhancement in Content Delivery Systems: The State-of-the-Art and Future Directions

Autor:	Lee, Royson, Venieris, Stylianos I., Lane, Nicholas D.
Rok vydání:	2020
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Distributed Parallel and Cluster Computing Electrical Engineering and Systems Science - Image and Video Processing
Druh dokumentu:	Working Paper
Popis:	Internet-enabled smartphones and ultra-wide displays are transforming a variety of visual apps spanning from on-demand movies and 360-degree videos to video-conferencing and live streaming. However, robustly delivering visual content under fluctuating networking conditions on devices of diverse capabilities remains an open problem. In recent years, advances in the field of deep learning on tasks such as super-resolution and image enhancement have led to unprecedented performance in generating high-quality images from low-quality ones, a process we refer to as neural enhancement. In this paper, we survey state-of-the-art content delivery systems that employ neural enhancement as a key component in achieving both fast response time and high visual quality. We first present the deployment challenges of neural enhancement models. We then cover systems targeting diverse use-cases and analyze their design decisions in overcoming technical challenges. Moreover, we present promising directions based on the latest insights from deep learning research to further boost the quality of experience of these systems. Comment: Accepted at the 1st Workshop on Distributed Machine Learning at CoNEXT 2020 (DistributedML 2020)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2010.05838 Zobrazit plný text záznamu View this record from Arxiv