Abstract
Multi-view video consists of multiple views which are shot simultaneously by multiple cameras from different viewpoints. Users can watch the video while choosing a viewpoint from them freely. In the multi-view video streaming, we need to deal with a problem that the multi-view videos put an enormous burden on the network for delivering because of transmitting many videos at a same time. Hierarchical multi-view streaming(HMVS) has been proposed as one of the methods of reducing the transmission quantity of multi-view video. The prediction of switching viewpoints is necessary for bandwidth allocation in HMVS. In this paper, we propose a viewpoint switching prediction model based on viewing logs for HMVS. The proposed model divides the sequence of multi-view video into some scenes and learns the probability of switching viewpoints for each scene from viewing logs. Through the evaluation experiment, the proposed model achieved effective viewpoint switching predictions.
