Applying VertexShuffle toward 360-degree video super-resolution

With the recent successes of deep learning models, the performance of 2D image super-resolution has improved significantly. Inspired by recent state-of-the-art 2D super-resolution models and spherical CNNs, in this paper, we design a novel spherical superresolution (SSR) approach for 360-degree videos. To address the bandwidth waste problem associated with 360-degree video transmission/streaming and save computation, we propose the Focused Icosahedral Mesh to represent a small area on the sphere and construct matrices to rotate spherical content to the focused mesh area. We also propose a novel VertexShuffle operation on the mesh, motivated by the 2D PixelShuffle operation. We compare our SSR approach with state-of-the-art 2D super-resolution models. We show that SSR has the potential to achieve significant benefits when applied to spherical signals.