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.

翻译：移动云游戏行业在过去十年中蓬勃发展。当流式传输的游戏视频从云服务器传输到用户的客户端设备时，能够在不依赖任何参考视频的情况下监控失真视频质量的算法成为理想工具。然而，构建能够准确预测计算机图形引擎渲染的流式游戏视频质量的无参考视频质量评估（NR VQA）模型是一项具有挑战性的问题，因为游戏内容通常在统计上与自然视频不同，往往缺乏细节且包含大量平滑区域。直到近期，因缺乏足够的移动游戏内容主观质量数据库，该问题变得更加复杂。我们创建了一种名为游戏视频质量评估器（GAMIVAL）的新型游戏专用无参考视频质量评估模型，该模型结合并利用了空间和时间游戏失真场景统计模型、神经噪声模型以及深度语义特征的优势。通过使用支持向量回归（SVR）作为回归器，GAMIVAL 在全新的LIVE-Meta移动云游戏（LIVE-Meta MCG）视频质量数据库上实现了卓越性能。