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：过去十年间，移动云游戏产业迅速发展。当流式游戏视频从云服务器传输至用户客户端设备时，能够无需任何参考视频即可监测失真视频质量的算法是极具价值的工具。然而，构建能够准确预测由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）模型是一项挑战性任务，因为游戏内容通常与自然视频在统计特性上存在差异，常缺乏细节并包含大量平滑区域。直至近期，由于缺乏充分的移动游戏内容主观质量数据库，该问题进一步复杂化。我们提出了一种新型游戏专用无参考视频质量评估模型——游戏视频质量评估器（GAMIVAL），该模型融合并利用了空间与时间维度的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。采用支持向量回归（SVR）作为回归器后，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优越性能。