Lossy Compression with Gaussian Diffusion

Understand

We consider a novel lossy compression approach based on unconditional diffusion generative models, which we call DiffC.

• Unlike modern compression schemes which rely on transform coding and quantization to restrict the transmitted information, DiffC relies on the efficient communication of pixels corrupted by Gaussian noise.
• We implement a proof of concept and find that it works surprisingly well despite the lack of an encoder transform, outperforming the state-of-the-art generative compression method HiFiC on ImageNet 64x64.
• DiffC only uses a single model to encode and denoise corrupted pixels at arbitrary bitrates.