基于视点预测的全景视频码率自适应策略

为解决全景视频传输中存在的视频卡顿多、用户体验质量（quality of experience，QoE）低等问题，研究当前主流的视点自适应传输方案，提出一种基于视点预测的码率自适应策略（VPBAS）。首先，构建了一种基于长短期记忆网络和全卷积网络的视点预测模型，模型将视点数据和视频显著性信息进行特征融合，实现不同模态数据的相互补充和修正，提高视点预测的准确率；然后，客户端采用随机森林算法预测当前的可用带宽，并根据视点预测结果和可用带宽信息为视频分块选择码率。最后，客户端把选择的码率信息定期发送给服务器，服务器根据反馈的信息向客户端推送最佳码率的全景视频流，这种交互过程在视频播放期间不断地重复，直至客户端观看完毕。实验结果表明，与现有传输方案相比，VPBAS能有效提高带宽受限情况下的视频观看体验。

Bitrate adaptive strategy of panoramic video based on viewpoint prediction

To solve the problems of video freezes and low quality of experience(QoE) in panoramic video transmission, this paper proposed a bitrate adaptive strategy based on viewpoint prediction(VPBAS) by studying the current popular viewpoint adaptive transmission scheme. Firstly, this paper constructed a viewpoint prediction model based on long short-term memory network and full convolutional network. The model concatenated the viewpoint data and video saliency information to realize the mutual complement and correction of different modal data, so as to improve the accuracy of viewpoint prediction. Secondly, the client used the random forest algorithm to predict the current available bandwidth, and selected the bitrate for video segments according to the prediction results and available bandwidth information. Finally, the client sent the selected bitrate information to the server regularly, and the server pushed the panoramic video stream of the best bitrate to the client according to the feedback information. It repeated this interactive process continuously during the video playback until the client finished watching. Experimental results show that VPBAS can effectively improve the user''s viewing experience under bandwidth limitation compared with other schemes.