BINR: Live Video Broadcasting Quality Assessment

Live video broadcasting has become widely accessible through popular platforms such as Instagram, Facebook, and YouTube, enabling real-time content sharing and user interaction. While the Quality of Experience (QoE) has been extensively studied for Video-on-Demand (VoD) services, the QoE of live broadcast videos remains relatively underexplored. In this paper, we address this gap by proposing a novel machine learning–based model for QoE prediction in live video broadcasting scenarios. Our approach, BiNR, introduces two models: BiNRfast, which uses only bitstream features for ultra-fast QoE predictions, and the full model BiNRfull, which integrates bitstream features with a pixel-based no-reference (NR) quality metric that works on the decoded signal. We evaluate multiple regression models to predict subjective QoE scores and further conduct feature importance analysis. Experimental results show that our full model achieves a Pearson Correlation Coefficient (PCC)/Spearman Rank Correlation Coefficient (SRCC) of 0.92/0.92 with subjective scores, significantly outperforming the state-of-the-art methods.