Quality-of-Experience for Adaptive Streaming Videos: An Expectation Confirmation Theory Motivated Approach

The dynamic adaptive streaming over HTTP (DASH) provides an inter-operable solution to overcome volatile network conditions, but how the human visual quality-ofexperience (QoE) changes with time-varying video quality is not well-understood. Here, we build a large-scale video database of time-varying quality and design a series of subjective experiments to investigate how humans respond to compression level, spatial and temporal resolution adaptations. Our path-analytic results show that quality adaptations influence the QoE by modifying the perceived quality of subsequent video segments. Specifically, the quality deviation introduced by quality adaptations is asymmetric with respect to the adaptation direction, which is further influenced by other factors such as compression level and content. Furthermore, we propose an objective QoE model by integrating the empirical findings from our subjective experiments and the expectation confirmation theory (ECT). Experimental results show that the proposed ECT-QoE model is in close agreement with subjective opinions and significantly outperforms existing QoE models. The video database together with the code are available online at https://ece.uwaterloo.ca/~zduanmu/tip2018ectqoe/.