We present the outcomes of a recent large-scale subjective study of Mobile
Cloud Gaming Video Quality Assessment (MCG-VQA) on a diverse set of gaming
videos. Rapid advancements in cloud services, faster video encoding
technologies, and increased access to high-speed, low-latency wireless internet
have all contributed to the exponential growth of the Mobile Cloud Gaming
industry. Consequently, the development of methods to assess the quality of
real-time video feeds to end-users of cloud gaming platforms has become
increasingly important. However, due to the lack of a large-scale public Mobile
Cloud Gaming Video dataset containing a diverse set of distorted videos with
corresponding subjective scores, there has been limited work on the development
of MCG-VQA models. Towards accelerating progress towards these goals, we
created a new dataset, named the LIVE-Meta Mobile Cloud Gaming (LIVE-Meta-MCG)
video quality database, composed of 600 landscape and portrait gaming videos,
on which we collected 14,400 subjective quality ratings from an in-lab
subjective study. Additionally, to demonstrate the usefulness of the new
resource, we benchmarked multiple state-of-the-art VQA algorithms on the
database. The new database will be made publicly available on our website:
https://live.ece.utexas.edu/research/LIVE-Meta-Mobile-Cloud-Gaming/index.html

本文通过一项大规模主观研究 Mobile Cloud Gaming Video Quality Assessment，提出了一个新数据集，并对现有算法进行了基准测试。

移动云游戏视频的主客观质量评估研究

Study of Subjective and Objective Quality Assessment of Mobile Cloud  Gaming Videos

The mobile cloud gaming industry has been rapidly growing over the last
decade. When streaming gaming videos are transmitted to customers' client
devices from cloud servers, algorithms that can monitor distorted video quality
without having any reference video available are desirable tools. However,
creating No-Reference Video Quality Assessment (NR VQA) models that can
accurately predict the quality of streaming gaming videos rendered by computer
graphics engines is a challenging problem, since gaming content generally
differs statistically from naturalistic videos, often lacks detail, and
contains many smooth regions. Until recently, the problem has been further
complicated by the lack of adequate subjective quality databases of mobile
gaming content. We have created a new gaming-specific NR VQA model called the
Gaming Video Quality Evaluator (GAMIVAL), which combines and leverages the
advantages of spatial and temporal gaming distorted scene statistics models, a
neural noise model, and deep semantic features. Using a support vector
regression (SVR) as a regressor, GAMIVAL achieves superior performance on the
new LIVE-Meta Mobile Cloud Gaming (LIVE-Meta MCG) video quality database.

该文研究了移动云游戏中针对没有参考视频的视频质量评估模型（No-Reference Video Quality Assessment model，NR VQA）的问题，并提出了一种新的 NR VQA 模型，使用支持向量回归（SVR）对其进行训练，取得了优异的性能（LIVE-Meta MCG 视频数据库）。