DSA-QoE: Quality of Experience Evaluation for Streaming Video Based on Dual-Stage Attention

Ziheng Jia, Xiongkuo Min, Guangtao Zhai

Abstract: With the rapid development of streaming media technology, the real-time streaming video Quality of Experience (QoE) assessment has become an important objective for creating new Adaptive Bitrate (ABR) algorithms. The QoE prediction on the client side is challenging considering the sophisticated perception mechanisms of humans, especially the human attention behaviors over time. To address this issue, we propose a learnable model based on the dual-stage attention mechanism to precisely predict continuous QoE which is not covered by most of the current related works. Given the close relationship between the continuous and overall QoE, we use a unified framework to predict these two indices. We have conducted comparison experiments on 6 open datasets, and our model shows superior performance.