关键词: 3D视频, 体验质量, 视频质量评价, 卷积神经网络(CNN), HTTP自适应流媒体

Abstract: The key for 3D video network service is to improve the quality of experience (QoE) of users, which can be, however, affected by mutable network conditions and video contents. For conventional 2D videos, the HTTP adaptive streaming (HAS) technique has demonstrated its significance in improving user QoE by utilizing dynamically switched bitrates, while for 3D video transmission with at least two video streams, this technique has not yet been extensively explored. Dynamic conversion policy of the video quality level is the core of HAS technique. In this work, we investigate the impact on user QoE when introducing dynamic bitrates to different 3D videos. A subjective database is built to illustrate the connection between block-level objective quality, which changes with bitrates, and the QoE of 3D vision. Through this, we propose a convolutional neural network (CNN) based QoE model that effectively assesses the QoE by block-level objective quality. The Pearson linear correlation coefficient (PLCC) of the model predictive value and the mean opinion score (MOS) is 0.906.The proposed framework can provide guidance to inter-view bitrate balancing of HAS for 3D video transmission.

Key words: 3D video, quality of experience, video quality assessment, convolutional neural network (CNN), HTTP adaptive streaming