  Low-light images frequently occur due to unavoidable environmental influences
or technical limitations, such as insufficient lighting or limited exposure
time. To achieve better visibility for visual perception, low-light image
enhancement is usually adopted. Besides, lossy image compression is vital for
meeting the requirements of storage and transmission in computer vision
applications. To touch the above two practical demands, current solutions can
be categorized into two sequential manners: ``Compress before Enhance (CbE)''
or ``Enhance before Compress (EbC)''. However, both of them are not suitable
since: (1) Error accumulation in the individual models plagues sequential
solutions. Especially, once low-light images are compressed by existing general
lossy image compression approaches, useful information (e.g., texture details)
would be lost resulting in a dramatic performance decrease in low-light image
enhancement. (2) Due to the intermediate process, the sequential solution
introduces an additional burden resulting in low efficiency. We propose a novel
joint solution to simultaneously achieve a high compression rate and good
enhancement performance for low-light images with much lower computational cost
and fewer model parameters. We design an end-to-end trainable architecture,
which includes the main enhancement branch and the signal-to-noise ratio (SNR)
aware branch. Experimental results show that our proposed joint solution
achieves a significant improvement over different combinations of existing
state-of-the-art sequential ``Compress before Enhance'' or ``Enhance before
Compress'' solutions for low-light images, which would make lossy low-light
image compression more meaningful. The project is publicly available at:
https://github.com/CaiShilv/Joint-IC-LL.
