  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.
