论文标题
内容传递系统的神经增强：最新的和未来的方向
Neural Enhancement in Content Delivery Systems: The State-of-the-Art and Future Directions
论文作者
论文摘要
支持互联网的智能手机和超宽显示屏正在将各种视觉应用转换为从点播电影和360度视频转换为视频会议和实时流媒体。但是，在各种功能的设备上，在波动的网络条件下，坚固地传达了视觉内容仍然是一个开放的问题。近年来，在超级分辨率和图像增强等任务的深入学习领域的进步导致了从低质量图像产生高质量图像的前所未有的表现，这是我们称为神经增强的过程。在本文中，我们调查了采用神经增强的最先进的内容输送系统，作为实现快速响应时间和高视觉质量的关键组成部分。我们首先提出神经增强模型的部署挑战。然后，我们介绍针对不同用例的系统，并在克服技术挑战中分析其设计决策。此外，我们根据深度学习研究的最新见解提出了有希望的方向，以进一步提高这些系统的经验质量。
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.
