  Lossless image compression is an essential research field in image
compression. Recently, learning-based image compression methods achieved
impressive performance compared with traditional lossless methods, such as
WebP, JPEG2000, and FLIF. However, there are still many impressive lossy
compression methods that can be applied to lossless compression. Therefore, in
this paper, we explore the methods widely used in lossy compression and apply
them to lossless compression. Inspired by the impressive performance of the
Gaussian mixture model (GMM) shown in lossy compression, we generate a lossless
network architecture with GMM. Besides noticing the successful achievements of
attention modules and autoregressive models, we propose to utilize attention
modules and add an extra autoregressive model for raw images in our network
architecture to boost the performance. Experimental results show that our
approach outperforms most classical lossless compression methods and existing
learning-based methods.
