论文标题
基于内容的个性化视口预测360度VR视频的深度学习
Deep Learning for Content-based Personalized Viewport Prediction of 360-Degree VR Videos
论文作者
论文摘要
在本文中，研究了虚拟现实视频的头部运动预测问题。在考虑的模型中，引入了深度学习网络，以利用位置数据以及视频框架内容来预测未来的头部运动。为了优化该神经网络中的数据输入，还探索了该模型的数据样本率，降低的数据和长期预测长度。仿真结果表明，与仅依赖位置数据的基线方法相比，所提出的方法在预测准确性方面产生16.1 \％的改善。
In this paper, the problem of head movement prediction for virtual reality videos is studied. In the considered model, a deep learning network is introduced to leverage position data as well as video frame content to predict future head movement. For optimizing data input into this neural network, data sample rate, reduced data, and long-period prediction length are also explored for this model. Simulation results show that the proposed approach yields 16.1\% improvement in terms of prediction accuracy compared to a baseline approach that relies only on the position data.
