JPEG is a popular image compression method widely used by individuals, data
center, cloud storage and network filesystems. However, most recent progress on
image compression mainly focuses on uncompressed images while ignoring
trillions of already-existing JPEG images. To compress these JPEG images
adequately and restore them back to JPEG format losslessly when needed, we
propose a deep learning based JPEG recompression method that operates on DCT
domain and propose a Multi-Level Cross-Channel Entropy Model to compress the
most informative Y component. Experiments show that our method achieves
state-of-the-art performance compared with traditional JPEG recompression
methods including Lepton, JPEG XL and CMIX. To the best of our knowledge, this
is the first learned compression method that losslessly transcodes JPEG images
to more storage-saving bitstreams.

本文介绍了基于深度学习的 JPEG 图像重压缩方法，能够在 DCT 域中进行操作，提出了多级交叉通道熵模型以压缩最具信息量的 Y 分量，在保证无损的前提下实现了 JPEG 图像转码为更节省存储空间的比特流，实验表明该方法在传统 JPEG 重压缩方法（Lepton，JPEG XL 和 CMIX）中表现出最先进的性能。