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Lossless image compression is an essential research field in imagecompression. Recently, learning-based image compression methods achievedimpressive performance compared with traditional lossless methods, such asWebP, JPEG2000, and FLIF. However, there are still many impressive lossycompression methods that can be applied to lossless compression. Therefore, inthis paper, we explore the methods widely used in lossy compression and applythem to lossless compression. Inspired by the impressive performance of theGaussian mixture model (GMM) shown in lossy compression, we generate a losslessnetwork architecture with GMM. Besides noticing the successful achievements ofattention modules and autoregressive models, we propose to utilize attentionmodules and add an extra autoregressive model for raw images in our networkarchitecture to boost the performance. Experimental results show that ourapproach outperforms most classical lossless compression methods and existinglearning-based methods.

Learned Lossless Image Compression With Combined Autoregressive Models And Attention Modules