Adaptation of ANN Based Video Stream QoE Prediction Model

Pseudo-Subjective Quality Assessment PSQA is an effective way to prediction the Quality of experience QoE of video stream. The ANN-based PSQA model gives a decent QoE prediction accuracy when it is tested under the same condition as training. However, the performance of the model under mismatched conditions is little studied, and how to effectively adapt the models from one condition to another is still an open question. In this work, we first evaluated the performance of the ANN-based QoE prediction model under mismatched conditions. Our study shows that the QoE prediction accuracy degrades significantly when the model is applied to conditions different from the training condition. Further, we developed a feature transformation based model adaptation method to adapt the model from one condition to another. Experiments results show that the QoE prediction accuracy under mismatched conditions can be improved substantially using as few as five data samples under the new condition for model adaptation.