In recent years, live video streaming has gained widespread popularity across various social media platforms. Quality of experience (QoE), which reflects end-users' satisfaction and overall experience, plays a critical role for media service providers to optimize large-scale live compression and transmission strategies to achieve perceptually optimal rate-distortion trade-off. Although many QoE metrics for video-on-demand (VoD) have been proposed, there remain significant challenges in developing QoE metrics for live video streaming. To bridge this gap, we conduct a comprehensive study of subjective and objective QoE evaluations for live video streaming. For the subjective QoE study, we introduce the first live video streaming QoE dataset, TaoLive QoE, which consists of $42$ source videos collected from real live broadcasts and $1,155$ corresponding distorted ones degraded due to a variety of streaming distortions, including conventional streaming distortions such as compression, stalling, as well as live streaming-specific distortions like frame skipping, variable frame rate, etc. Subsequently, a human study was conducted to derive subjective QoE scores of videos in the TaoLive QoE dataset. For the objective QoE study, we benchmark existing QoE models on the TaoLive QoE dataset as well as publicly available QoE datasets for VoD scenarios, highlighting that current models struggle to accurately assess video QoE, particularly for live content. Hence, we propose an end-to-end QoE evaluation model, Tao-QoE, which integrates multi-scale semantic features and optical flow-based motion features to predicting a retrospective QoE score, eliminating reliance on statistical quality of service (QoS) features.

翻译：近年来，直播视频在各类社交媒体平台得到广泛普及。体现终端用户满意度与整体体验的体验质量（QoE），在媒体服务商优化大规模直播压缩与传输策略、实现感知最优率失真权衡中发挥关键作用。尽管针对视频点播（VoD）已提出诸多QoE指标，但在开发适用于直播视频的QoE度量方法方面仍存在显著挑战。为填补这一空白，我们对直播视频的主观与客观QoE评估展开全面研究。在主观QoE研究方面，我们首次构建了直播视频QoE数据集TaoLive QoE，该数据集包含42个源自真实直播节目的源视频，以及因各类流媒体失真退化的1,155个对应失真视频，涵盖压缩、卡顿等传统流媒体失真，以及帧跳过、可变帧率等直播特定失真。随后开展人类主观实验以获取TaoLive QoE数据集中视频的主观QoE评分。在客观QoE研究方面，我们在TaoLive QoE数据集及公开的VoD场景QoE数据集上对现有QoE模型进行基准测试，揭示当前模型难以准确评估视频QoE，尤其对直播内容效果欠佳。为此，我们提出端到端QoE评估模型Tao-QoE，该模型融合多尺度语义特征与基于光流的运动特征，实现回顾性QoE评分预测，摆脱对统计服务质量（QoS）特征的依赖。