Quality of Experience Evaluation for Streaming Video Using CGNN

Abstract

One of the principal contradictions these days in the field of video i s lying between the booming demand for evaluating the streaming video quality and the low precision of the Quality of Experience prediction results. In this paper, we propose Convolutional Neural Network and Gate Recurrent Unit (CGNN)-QoE, a deep learning QoE model, that can predict overall and continuous scores of video streaming services accurately in real time. We further implement state-of-the-art models on the basis of their works and compare with our method on six public available datasets. In all considered scenarios, the CGNN-QoE outperforms existing methods.

Publication
2020 IEEE International Conference on Visual Communications and Image Processing (VCIP)
Yu Dong
Yu Dong
PhD Student

I’m a Research PHD candidate at SJTU Media Lab. I’m doing my research on video processing and low latency end-to-end video systems, under the direction of Prof. Li Song.

Li Song
Li Song
Professor, IEEE Senior Member

Professor, Doctoral Supervisor, the Deputy Director of the Institute of Image Communication and Network Engineering of Shanghai Jiao Tong University, the Double-Appointed Professor of the Institute of Artificial Intelligence and the Collaborative Innovation Center of Future Media Network, the Deputy Secretary-General of the China Video User Experience Alliance and head of the standards group.