The dynamic adaptive streaming over HTTP provides an inter-operable solution to overcome the volatile network conditions, but its complex characteristic brings new challenges to objective video quality-of-experience (QoE) measurement. To test the generalizability and to facilitate the wide usage of QoE measurement techniques in real-world applications, we establish a new database named Waterloo Streaming QoE Database III (SQoE-III).
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.