Abstract
Currently, video streaming services put more emphasis on user feeling or satisfaction than before. How to design suitable model and algorithm to effectively and efficiently evaluate user quality of experience (QoE) has become a significant technical challenge. To get over this dilemma, this paper proposes the broad forest, a non-neural network style broad model for streaming video QoE evaluation. The design target of broad forest is to take advantage of representation potential of forest and low complexity characteristic of broad learning system. Specifically, we first give the construction of broad forest. Then, the associated incremental learning algorithm for efficiently supporting the added structure and inputs is designed. Finally, we apply the broad forest to streaming video QoE evaluation. Experimental results show that the broad forest can not only guarantee accuracy, but also decrease training time. When subjective features are considered, it can further promote performance of QoE evaluation.
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