Pseudocylindrical Convolutions for Learned Omnidirectional Image Compression

Equirectangular projection (ERP) is a convenient form to store omnidirectional images, but it is neither equal-area nor conformal, creating challenges for subsequent visual communication. When used for image compression, ERP amplifies sampling density and deforms objects near the poles, hindering perceptually optimal bit allocation. Here, we present one of the earliest endeavors to apply deep neural networks to omnidirectional image compression. We first propose parametric pseudocylindrical representations that generalize common pseudocylindrical map projections. A tractable greedy algorithm is introduced to identify (sub-)optimal representation configurations, guided by a proxy objective for rate-distortion performance. We then develop pseudocylindrical convolutions, which can be efficiently implemented by standard convolutions with 'pseudocylindrical padding.' To demonstrate the utility of the proposed pseudocylindrical representations and convolutions, we implement an end-to-end omnidirectional image compression method, consisting of an analysis transform, a uniform quantizer, a synthesis transform, and an entropy model. Experiments show that our optimized method achieves consistently better rate-distortion performance compared to the state-of-the-art.