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.

翻译：图像噪声在摄影中无处不在。然而，图像噪声既不可压缩也不受欢迎，因此在压缩图像比特流中试图传递噪声会在码率和失真两方面都产生次优结果。我们提出在训练编解码器时显式地学习图像去噪任务。为此，我们利用自然图像噪声数据集，该数据集提供了涵盖不同ISO数值下捕获的多种场景，从而产生不同水平的噪声（包括不显著的噪声）。基于此训练集，我们使用含噪-干净图像对来监督编解码器，并证明仅通过基于可变噪声水平图像混合训练的单模型，在处理含噪和干净图像时均能取得同类最佳结果，其率失真性能优于纯压缩模型，甚至优于几乎减少一个数量级GMac运算量的去噪后再压缩模型对。