GAMIVAL：移动云游戏内容的视频质量预测
GAMIVAL: Video Quality Prediction on Mobile Cloud Gaming Content
AI总结：
GAMIVAL通过结合空间和时间失真场景统计模型、神经噪声模型和深度语义特征，实现对移动云游戏内容视频质量的高效预测。
摘要：
在过去十年中，移动云游戏行业迅速发展。当将流媒体游戏视频从云服务器传输到客户终端设备时，能够监控失真视频质量且没有可用参考视频的算法是理想的工具。然而，创建能够准确预测由计算机图形引擎渲染的流媒体游戏视频质量的无参考视频质量评估（NR VQA）模型是一个具有挑战性的问题，因为游戏内容通常在统计上与自然视频不同，往往细节较少，并包含许多平滑区域。直到最近，该问题变得更加复杂，因为缺乏足够的主观质量数据库来移动游戏内容。我们创建了一个新的专门用于游戏的NR VQA模型，称为游戏视频质量评估器（GAMIVAL），该模型结合并利用了空间和时间游戏失真场景统计模型、神经噪声模型和深度语义特征。使用支持向量回归（SVR）作为回归器，GAMIVAL在新的LIVE-Meta移动云游戏（LIVE-Meta MCG）视频质量数据库上实现了优越的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
