论文标题
流式游戏视频的主观和客观分析
Subjective and Objective Analysis of Streamed Gaming Videos
论文作者
论文摘要
在线用户生成的内容（UGC）以流式传输和共享视频的形式不断提高，加快了感知视频质量评估（VQA）模型的开发，可用于帮助优化其交付。当熟练的游戏玩家发布游戏玩法的视频时，创建了一种相对较新的UGC视频，游戏视频是相对较新的视频。 UGC游戏视频的这类屏幕截图已在YouTube和Twitch等主要流媒体平台上非常受欢迎。合成生成的游戏内容对现有的VQA算法提出了挑战，包括基于自然场景/视频统计模型的挑战。合成生成的游戏内容与自然视频相比，统计行为不同。许多研究旨在了解在游戏视频流，在线游戏和云游戏中产生的专业生成游戏视频的感知特征。但是，在了解UGC游戏视频的质量以及如何表征和预测方面，几乎没有做出的工作。为了提高游戏视频VQA模型开发的进度，我们对UGC游戏视频的主观和客观VQA模型进行了全面研究。为此，我们创建了一个新颖的UGC游戏视频资源，称为Live-Youtube游戏视频质量（Live-YT-Gaming）数据库，该数据库由600个真正的UGC游戏视频组成。我们对这些数据进行了一项主观人类研究，产生了61名人类受试者记录的18,600个人类质量评级。我们还根据自然视频统计信息和CNN-Learned功能评估了新数据库中的许多最先进（SOTA）VQA模型，其中包括一个名为Game-VQP的新数据库。为了帮助支持该领域的工作，我们正在制作新的Live-yt-Gaming数据库，该数据库通过链接：https：//live.ece.utexas.edu/research/live-yt-gaming/index.html公开获得。
The rising popularity of online User-Generated-Content (UGC) in the form of streamed and shared videos, has hastened the development of perceptual Video Quality Assessment (VQA) models, which can be used to help optimize their delivery. Gaming videos, which are a relatively new type of UGC videos, are created when skilled gamers post videos of their gameplay. These kinds of screenshots of UGC gameplay videos have become extremely popular on major streaming platforms like YouTube and Twitch. Synthetically-generated gaming content presents challenges to existing VQA algorithms, including those based on natural scene/video statistics models. Synthetically generated gaming content presents different statistical behavior than naturalistic videos. A number of studies have been directed towards understanding the perceptual characteristics of professionally generated gaming videos arising in gaming video streaming, online gaming, and cloud gaming. However, little work has been done on understanding the quality of UGC gaming videos, and how it can be characterized and predicted. Towards boosting the progress of gaming video VQA model development, we conducted a comprehensive study of subjective and objective VQA models on UGC gaming videos. To do this, we created a novel UGC gaming video resource, called the LIVE-YouTube Gaming video quality (LIVE-YT-Gaming) database, comprised of 600 real UGC gaming videos. We conducted a subjective human study on this data, yielding 18,600 human quality ratings recorded by 61 human subjects. We also evaluated a number of state-of-the-art (SOTA) VQA models on the new database, including a new one, called GAME-VQP, based on both natural video statistics and CNN-learned features. To help support work in this field, we are making the new LIVE-YT-Gaming Database, publicly available through the link: https://live.ece.utexas.edu/research/LIVE-YT-Gaming/index.html .
