Learning Compressible 360deg Video Isomers

Abstract

Standard video encoders developed for conventional narrow field-of-view video are widely applied to 360deg video as well, with reasonable results. However, while this approach commits arbitrarily to a projection of the spherical frames, we observe that some orientations of a 360deg video, once projected, are more compressible than others. We introduce an approach to predict the sphere rotation that will yield the maximal compression rate. Given video clips in their original encoding, a convolutional neural network learns the association between a clip's visual content and its compressibility at different rotations of a cubemap projection. We validate our idea on thousands of video clips and multiple popular video codecs. The results show that this untapped dimension of 360deg video compression has substantial potential--"good" rotations are typically 8-10% more compressible than bad ones, and our learning approach can predict them reliably 82% of the time. The full report is published in the CVPR main conference.