  Image noise is ubiquitous in photography. However, image noise is not
compressible nor desirable, thus attempting to convey the noise in compressed
image bitstreams yields sub-par results in both rate and distortion. We propose
to explicitly learn the image denoising task when training a codec. Therefore,
we leverage the Natural Image Noise Dataset, which offers a wide variety of
scenes captured with various ISO numbers, leading to different noise levels,
including insignificant ones. Given this training set, we supervise the codec
with noisy-clean image pairs, and show that a single model trained based on a
mixture of images with variable noise levels appears to yield best-in-class
results with both noisy and clean images, achieving better rate-distortion than
a compression-only model or even than a pair of denoising-then-compression
models with almost one order of magnitude fewer GMac operations.
