On the Importance of Denoising when Learning to Compress Images
- URL: http://arxiv.org/abs/2307.06233v1
- Date: Wed, 12 Jul 2023 15:26:04 GMT
- Title: On the Importance of Denoising when Learning to Compress Images
- Authors: Benoit Brummer and Christophe De Vleeschouwer
- Abstract summary: We propose to explicitly learn the image denoising task when training a.
We leverage the Natural Image Noise dataset, which offers a wide variety of scenes captured with various ISO numbers.
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.
- Score: 34.99683302788977
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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