A Multi-Layer End-to-End 360 Image Compression

360° images have attracted increasing attention due to their wide field of view. However, spherical 360° images need to be converted into 2D equi-rectangular projection (ERP) images for compression. This conversion often leads to pixel overstretching in the ERP image, which results in a lot of redundancy in the texture. Performing a direct prediction without considering the stretching will inevitably make it difficult to achieve optimal results. To tackle this problem, we propose a multi-layer adaptive scale-block scheme for ERP image compression. In particular, we introduce a multi-layer structure based on the overstretching rate and use multi-scale convolution kernels to better match each layer and extract features more effectively. Subsequently, we employ an adaptive scale-block method to effectively reduce bitrate redundancy in overstretched and less important regions. Finally, we propose a new end-to-end model that is efficient for 360° image compression. Experimental results demonstrate that our scheme outperforms other image compression methods and reduces bitrate by nearly 16% compared to the latest learned 360° image compression model.