Feature-based prediction of streaming video QoE: Distortions, stalling and memory

Towards more effectively predicting user QoE, we have developed a QoE prediction model called Video Assessment of TemporaL Artifacts and Stalls (Video ATLAS), which is a feature-based approach that combines a number of QoE-related features, including perceptually-relevant quality features, stalling-aware features and memory-driven features to make QoE predictions. We evaluated Video ATLAS on the recently designed LIVE-Netflix Video QoE Database which consists of practical playout patterns, where the videos are afflicted by both quality changes and stalling events, and found that it provides improved performance over state-of-the-art video quality metrics while generalizing well on a different dataset. The proposed algorithm is made publicly available at http://live.ece.utexas.edu/research/VideoATLAS/vatlas_index.html.