  When it comes to image compression in digital cameras, denoising is
traditionally performed prior to compression. However, there are applications
where image noise may be necessary to demonstrate the trustworthiness of the
image, such as court evidence and image forensics. This means that noise itself
needs to be coded, in addition to the clean image itself. In this paper, we
present a learning-based image compression framework where image denoising and
compression are performed jointly. The latent space of the image codec is
organized in a scalable manner such that the clean image can be decoded from a
subset of the latent space (the base layer), while the noisy image is decoded
from the full latent space at a higher rate. Using a subset of the latent space
for the denoised image allows denoising to be carried out at a lower rate.
Besides providing a scalable representation of the noisy input image,
performing denoising jointly with compression makes intuitive sense because
noise is hard to compress; hence, compressibility is one of the criteria that
may help distinguish noise from the signal. The proposed codec is compared
against established compression and denoising benchmarks, and the experiments
reveal considerable bitrate savings compared to a cascade combination of a
state-of-the-art codec and a state-of-the-art denoiser.
