基于HTTP自适应流媒体传输的3D视频质量评价

3D视频网络服务的关键在于提高用户的体验质量（QoE），而体验质量往往会由于网络环境的变化及视频内容的不同而受到影响。传统的2D视频传输可以采用基于HTTP的自适应流媒体（HAS）速率自适应机制有效地利用网络带宽，提高用户体验质量。因此对于如何利用动态自适应流媒体技术实现至少需要传输两路视频流的3D网络视频服务已经越来越被关注。HAS技术的关键在于媒体质量级别的动态转换策略，主要研究了3D视频中不同视点比特率的变化对用户观看体验质量的影响。首先，建立一个主观数据库探讨块级客观质量与3D视频的视觉体验质量之间的关系，块级客观质量将随着比特率的变化而变化。其次，提出了一种基于卷积神经网络（CNN）的QoE模型，该模型可以通过块级客观质量有效地评估QoE，模型预测值和平均意见分（MOS）的皮尔森线性相关系数（PLCC）为0.906，可在自适应流媒体应用中为3D视频传输中不同视点的码率调整提供指导。 

3D video quality evaluation based on adaptive streaming over HTTP

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.