A Critical Analysis of Deep Learning Methods for Video QoE Prediction

Multimedia video applications significantly impact video quality prediction, widely regarded as one of the most challenging problems. The Quality of Experience (QoE) prediction of the video mimics the satisfaction of the content of the video as humans perceive it. Machine learning and deep learning models have applied numerous methods to obtain QoE predictions. Some of these methods are full reference or reduced reference (half reference); others are no reference. In this paper, we attempt to explore, evaluate, and analyze the different scenarios and models related to QoE predictions for videos using deep learning. We have conducted a comprehensive examination to address the limitations of the existing models. Moreover, we suggest a new framework to overcome the limitations of the existing models.