Spherical Transformer for Spherical Image Compression

We present a novel spherical image compression approach based on Spherical Transformer Block(STB) that takes advantage of HEALPix's hierarchical structure, as illustrated in Figure 1. More specifically, we exploit the hierarchical properties of HEALPix to partition the sphere into several smaller windows and apply multi-head attention within each window, thereby alleviating the computational burden of the spherical Transformer. However, as there is no communication between neighboring windows, we incorporate spherical convolution to aggregate information from adjacent windows. In addition, we propose an inverted residual bottleneck module for feature embedding and a feedforward neural network. Experimental results show that our method outperforms the OSLO [1] approach in terms of performance, while also achieving faster codec times.