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- W4319300677 abstract "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." @default.
- W4319300677 created "2023-02-07" @default.
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- W4319300677 date "2023-01-01" @default.
- W4319300677 modified "2023-10-17" @default.
- W4319300677 title "On the Importance of Denoising when Learning to Compress Images" @default.
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- W4319300677 doi "https://doi.org/10.1109/wacv56688.2023.00247" @default.
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