Learning Compressible 360{\deg} Video Isomers

TL;DR

This paper presents a neural network-based method to predict the most compressible orientation of 360-degree videos, significantly improving compression efficiency by selecting optimal sphere rotations without multiple renderings.

Contribution

It introduces a learning-based approach to identify the best sphere rotation for compressing 360-degree videos, enhancing compression rates over standard methods.

Findings

Good rotations are 8-10% more compressible.

The approach predicts optimal rotations with 82% accuracy.

Validation on thousands of clips shows substantial compression gains.

Abstract

Standard video encoders developed for conventional narrow field-of-view video are widely applied to 360{\deg} 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 360{\deg} 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. Given a novel video, our learning-based approach efficiently infers the most compressible direction in one shot, without repeated rendering and compression of the source video. We validate our idea on thousands of video clips and multiple popular video codecs. The results show that this untapped dimension of 360{\deg} 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.