Related papers: Learned Lossless Image Compression With Combined Autoregressive Models And Attention Modules

Learned Lossless Image Compression With Combined Autoregressive Models And Attention Modules

URL: http://arxiv.org/abs/2208.13974v1
Date: Tue, 30 Aug 2022 03:27:05 GMT
Title: Learned Lossless Image Compression With Combined Autoregressive Models And Attention Modules
Authors: Ran Wang, Jinming Liu, Heming Sun, Jiro Katto
Abstract summary: Lossless image compression is an essential research field in image compression. Recent learning-based image compression methods achieved impressive performance. In this paper, we explore the methods widely used in lossy compression and apply them to lossless compression.
Score: 22.213840578221678
License: http://creativecommons.org/licenses/by/4.0/
Abstract: 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.

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