Joint Image Compression and Denoising via Latent-Space Scalability
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When it comes to image compression in digital cameras, denoising is traditionally performed prior to compression. However, there are applications where image noise may be necessary to demonstrate the trustworthiness of the image, such as court evidence and image forensics. This means that noise itself needs to be coded, in addition to the clean image itself. In this paper, we present a learnt image compression framework where image denoising and compression are performed jointly. The latent space of the image codec is organized in a scalable manner such that the clean image can be decoded from a subset of the latent space at a lower rate, while the noisy image is decoded from the full latent space at a higher rate. The proposed codec is compared against established compression and denoising benchmarks, and the experiments reveal considerable bitrate savings of up to 80 compression and denoising.
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