High efficient VR video coding based on auto projection selection using transferable features

![CDATA[Given multiple texture projection methods from the sphere surface to the planar surface, this paper proposes an adaptive selection mode that automatically chooses the appropriate projection method to obtain high compression efficiency of the VR video. The video compression efficiency is inherently affected by the video content, which is closely related to the projection method in the case of VR video encoding. In order to represent the VR video content in a compact manner, a feature vector (transferable feature) for each frame is extracted by a Res-CNN which is pre-trained by a large scale data set for general classification. Afterwards, the relation between the feature and the optimal projection method is investigated by using PCA-KNN, which can project the initial feature vector to a subspace where the VR videos can be efficiently classified with low ambiguity. The experimental results show that the proposed method can select the appropriate projection method that generates the best BD rate.]]