The increasing popularity of video gaming competitions, the so called eSports, has contributed to the rise of a new type of end-user: the passive game video streaming (GVS) user. This user acts as a passive spectator of the gameplay rather than actively interacting with the content. This content, which is streamed over the Internet, can suffer from disturbing network and encoding impairments. Therefore, assessing the user’s perceived quality, i.e the Quality of Experience (QoE), in real-time becomes fundamental. For the case of natural video content, several approaches already exist that tackle the client-side real-time QoE evaluation. The intrinsically different expectations of the passive GVS user, however, call for new real-time quality models for these streaming services. Therefore, this paper presents a real-time Reduced-Reference (RR) quality assessment framework based on a low-complexity psychometric curve-fitting approach. The proposed solution selects the most relevant, low-complexity objective feature. Afterwards, the relationship between this feature and the ground-truth quality is modelled based on the psychometric perception of the human visual system (HVS). This approach is validated on a publicly available dataset of streamed game videos and is benchmarked against both subjective scores and objective models. As a side contribution, a thorough accuracy analysis of existing Objective Video Quality Metrics (OVQMs) applied to passive GVS is provided. Furthermore, this analysis has led to interesting insights on the accuracy of low-complexity client-based metrics as well as to the creation of a new Full-Reference (FR) objective metric for GVS, i.e. the Game Video Streaming Quality Metric (GVSQM).

视频游戏竞赛（所谓的电子竞技）的日益普及推动了新型最终用户的崛起：被动游戏视频流（GVS）用户。该用户充当游戏玩法的被动观众，而不是主动与内容进行交互。通过互联网流式传输的此内容可能会受到网络干扰和编码障碍的影响。因此，评估用户的感知质量，即实时的体验质量（QoE）变得至关重要。对于自然视频内容，已经存在几种解决客户端实时QoE评估的方法。但是，被动GVS用户的内在不同的期望要求为这些流服务提供新的实时质量模型。因此，本文提出了一种基于低复杂度心理测度曲线拟合方法的实时减少参考质量评估框架。提出的解决方案选择了最相关的，低复杂度的目标特征。然后，基于人类视觉系统（HVS）的心理计量感知，对该特征与地面真实质量之间的关系进行建模。该方法在流媒体游戏视频的公开数据集上得到了验证，并针对主观得分和客观模型进行了基准测试。作为附带的贡献，提供了对应用于无源GVS的现有客观视频质量指标（OVQM）的全面准确性分析。此外，这项分析还为基于低复杂度客户端的指标的准确性带来了有趣的见解，并为GVS创建了新的全参考（FR）客观指标，即游戏视频流质量指标（GVSQM）。