On the Importance of Denoising When Learning To Compress Images

Benoit Brummer, Christophe De Vleeschouwer; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023, pp. 2440-2448

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 the codec. Therefore, we leverage the Natural Image Noise Dataset, which offers a wide variety of scenes captured with various noise levels. Given this training set, we 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.