The Waterloo Streaming Quality-of-Experience Database-IV

The diversity of video delivery pipeline poses a grand challenge to the evaluation of adaptive bitrate (ABR) streaming algorithms and objective quality-of-experience (QoE) models. Here we introduce so-far the largest subject-rated database of its kind, namely WaterlooSQoE-IV, consisting of 1350 adaptive streaming videos created from diverse source contents, video encoders, network traces, ABR algorithms, and viewing devices. We collect human opinions for each video with a series of carefully designed subjective experiments. Subsequent data analysis and testing/comparison of ABR algorithms and QoE models using the database lead to a series of novel observations and interesting findings, in terms of the effectiveness of subjective experiment methodologies, the interactions between user experience and source content, viewing device and encoder type, the heterogeneities in the bias and preference of user experiences, the behaviors of ABR algorithms, and the performance of objective QoE models.

视频传输管道的多样性对自适应比特率（ABR）流算法和客观用户体验（QoE）模型的评估构成了重大挑战。在此，我们推出了迄今为止规模最大的此类主观评分数据库——WaterlooSQoE-IV，该数据库包含由多样化的源内容、视频编码器、网络跟踪、ABR算法和观看设备创建的1350个自适应流视频。我们通过对每个视频进行一系列精心设计的主观实验，收集了人类意见。利用该数据库对ABR算法和QoE模型进行后续数据分析、测试和比较，我们发现了一系列新颖的观察和有趣的结果，涉及主观实验方法的有效性、用户体验与源内容、观看设备与编码器类型之间的相互作用、用户体验偏见和偏好的异质性、ABR算法的行为以及客观QoE模型的性能。

搜集汇总

背景与挑战

背景概述

Waterloo流媒体体验质量数据库-IV是目前同类型中最大的主观评分数据库，包含1350个由多种源内容、编码器、网络条件和设备组合生成的自适应流媒体视频，用于评估流媒体算法和体验质量模型。数据集包含视频文件和相关元数据如比特率、缓冲时长和主观评分(MOS)。