论文标题
通过组合自回归模型和注意力模块的学习无损图像压缩
Learned Lossless Image Compression With Combined Autoregressive Models And Attention Modules
论文作者
论文摘要
无损图像压缩是图像压缩中必不可少的研究领域。最近，与传统的无损方法（例如WebP，JPEG2000和FLIF）相比，基于学习的图像压缩方法具有令人印象深刻的性能。但是，仍然有许多令人印象深刻的有损压缩方法可以应用于无损压缩。因此，在本文中，我们探讨了广泛用于有损压缩的方法，并将其应用于无损压缩。受损失压缩显示的高斯混合模型（GMM）的令人印象深刻的性能的启发，我们与GMM生成了无损网络体系结构。除了注意到注意模块和自回归模型的成功成就外，我们还建议利用注意模块，并为我们的网络体系结构中的原始图像添加额外的自动回归模型，以提高性能。实验结果表明，我们的方法表现优于大多数古典无损压缩方法和现有基于学习的方法。
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.
