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

TL;DR

This paper surveys how neural enhancement techniques, powered by deep learning, are integrated into content delivery systems to improve visual quality and response times across diverse devices and network conditions.

Contribution

It provides a comprehensive overview of current neural enhancement-based content delivery systems, analyzing deployment challenges and future research directions.

Findings

Neural enhancement improves visual quality in content delivery.

Deployment challenges include computational constraints and network variability.

Future directions involve leveraging latest deep learning insights for better performance.

Abstract

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.