  Image compression and denoising represent fundamental challenges in image
processing with many real-world applications. To address practical demands,
current solutions can be categorized into two main strategies: 1) sequential
method; and 2) joint method. However, sequential methods have the disadvantage
of error accumulation as there is information loss between multiple individual
models. Recently, the academic community began to make some attempts to tackle
this problem through end-to-end joint methods. Most of them ignore that
different regions of noisy images have different characteristics. To solve
these problems, in this paper, our proposed signal-to-noise ratio~(SNR) aware
joint solution exploits local and non-local features for image compression and
denoising simultaneously. We design an end-to-end trainable network, which
includes the main encoder branch, the guidance branch, and the signal-to-noise
ratio~(SNR) aware branch. We conducted extensive experiments on both synthetic
and real-world datasets, demonstrating that our joint solution outperforms
existing state-of-the-art methods.
