Quality Estimation Models for Gaming Video Streaming Services Using Perceptual Video Quality Dimensions

被引：20
|
作者：
Zadtootaghaj, Saman [1 ]
Schmidt, Steven [1 ]
Sabet, Saeed Shafiee [2 ]
Moeller, Sebastian [1 ,3 ]
Griwodz, Carsten [4 ]
机构：
[1] TU Berlin, Qual & Usabil Lab, Berlin, Germany
[2] Simula, SimulaMet, Oslo, Norway
[3] DFKI Projektbur, Berlin, Germany
[4] Univ Oslo, Dept Informat, Oslo, Norway
来源：
MMSYS'20: PROCEEDINGS OF THE 2020 MULTIMEDIA SYSTEMS CONFERENCE | 2020年
基金：
欧盟地平线“2020”;
关键词：
Gaming Video Streaming; Quality Assessment; Dataset;
D O I：
10.1145/3339825.3391872
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The gaming industry is one of the largest digital markets for decades and is steady developing as evident by new emerging gaming services such as gaming video streaming, online gaming, and cloud gaming. While the market is rapidly growing, the quality of these services depends strongly on network characteristics as well as resource management. With the advancement of encoding technologies such as hardware accelerated engines, fast encoding is possible for delay sensitive applications such as cloud gaming. Therefore, already existing video quality models do not offer a good performance for cloud gaming applications. Thus, in this paper, we provide a gaming video quality dataset that considers hardware accelerated engines for video compression using the H.264 standard. In addition, we investigate the performance of signal-based and parametric video quality models on the new gaming video dataset. Finally, we build two novel parametric-based models, a planning and a monitoring model, for gaming quality estimation. Both models are based on perceptual video quality dimensions and can be used to optimize the resource allocation of gaming video streaming services.
引用
收藏
页码：213 / 224
页数：12
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