Science, Americas, Special Report

[Netflixlnen]

In addition, approaches have been proposed to combine salient features with users’ historical FoV information to predict future FoVs. ’s history information is considered, which introduces uncertainty that may affect the prediction accuracy of FoV, especially in multicast scenarios. This is the case because the single user’s attention may shift for a short time during the viewing procedure, resulting in historical trajectories that may not exactly match with video saliency. However, it is possible to use FoV feedback from a limited number of users combined with 360-degree video saliency detection to make more accurate FoV predictions in multiuser scenarios. To this end, this paper proposes a spherical convolution-based FoV prediction method, SPVP360, in 360-degree video multicast scenarios with limited FoV feedback, combining 360-degree video saliency as the main feature and several users’ FoVs as supplementary features. Specifically, a SPherical Convolutional Neural Network (SPCNN) is used instead of the traditional 2D CNN for saliency detection and FoV prediction to eliminate projection error.




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