Related papers: Lossy Compression with Gaussian Diffusion

Lossy Compression with Gaussian Diffusion

URL: http://arxiv.org/abs/2206.08889v1
Date: Fri, 17 Jun 2022 16:46:31 GMT
Title: Lossy Compression with Gaussian Diffusion
Authors: Lucas Theis, Tim Salimans, Matthew D. Hoffman, Fabian Mentzer
Abstract summary: We describe a novel lossy compression approach called DiffC which is based on unconditional diffusion generative models. We implement a proof of concept and find that it works surprisingly well despite the lack of an encoder transform. We show that a flow-based reconstruction achieves a 3 dB gain over ancestral sampling at highs.
Score: 28.930398810600504
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: We describe a novel lossy compression approach called DiffC which is based on unconditional diffusion generative models. 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. The approach further provides support for progressive coding, that is, decoding from partial bit streams. We perform a rate-distortion analysis to gain a deeper understanding of its performance, providing analytical results for multivariate Gaussian data as well as initial results for general distributions. Furthermore, we show that a flow-based reconstruction achieves a 3 dB gain over ancestral sampling at high bitrates.

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