Advances in Diffusion-Based Generative Compression

• URL: http://arxiv.org/abs/2601.18932v1
• Date: Mon, 26 Jan 2026 20:10:29 GMT
• Title: Advances in Diffusion-Based Generative Compression
• Authors: Yibo Yang, Stephan Mandt,
• Abstract summary: diffusion and related methods for generative modeling have found widespread success in visual media applications.<n>This article provides a unifying review of recent diffusion-based methods for generative lossy compression.
• Score: 39.89941842329785
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Popularized by their strong image generation performance, diffusion and related methods for generative modeling have found widespread success in visual media applications. In particular, diffusion methods have enabled new approaches to data compression, where realistic reconstructions can be generated at extremely low bit-rates. This article provides a unifying review of recent diffusion-based methods for generative lossy compression, with a focus on image compression. These methods generally encode the source into an embedding and employ a diffusion model to iteratively refine it in the decoding procedure, such that the final reconstruction approximately follows the ground truth data distribution. The embedding can take various forms and is typically transmitted via an auxiliary entropy model, and recent methods also explore the use of diffusion models themselves for information transmission via channel simulation. We review representative approaches through the lens of rate-distortion-perception theory, highlighting the role of common randomness and connections to inverse problems, and identify open challenges.

Related papers

• Generative Image Compression by Estimating Gradients of the Rate-variable Feature Distribution [37.60572296105984]
  We propose a novel diffusion-based generative modeling framework tailored for generative image compression.<n>A reverse neural network is trained to reconstruct images by reversing the compression process directly.<n>This approach achieves smooth rate adjustment and photo-realistic reconstructions with only a minimal number of sampling steps.
  arXiv  Detail & Related papers  (2025-05-27T10:18:24Z)
• Diffusion-based Perceptual Neural Video Compression with Temporal Diffusion Information Reuse [45.134271969594614]
  DiffVC is a diffusion-based perceptual neural video compression framework.<n>It integrates foundational diffusion model with the video conditional coding paradigm.<n>We show that our proposed solution delivers excellent performance in both perception metrics and visual quality.
  arXiv  Detail & Related papers  (2025-01-23T10:23:04Z)
• Progressive Compression with Universally Quantized Diffusion Models [35.199627388957566]
  We explore the potential of diffusion models for progressive coding, resulting in a sequence of bits that can be incrementally transmitted and decoded.<n>Unlike prior work based on Gaussian diffusion or conditional diffusion models, we propose a new form of diffusion model with uniform noise in the forward process.<n>We obtain promising first results on image compression, achieving competitive rate-distortion and rate-realism results on a wide range of bit-rates with a single model.
  arXiv  Detail & Related papers  (2024-12-14T19:06:01Z)
• Learning Diffusion Model from Noisy Measurement using Principled Expectation-Maximization Method [9.173055778539641]
  We propose a principled expectation-maximization (EM) framework that iteratively learns diffusion models from noisy data with arbitrary corruption types. Our framework employs a plug-and-play Monte Carlo method to accurately estimate clean images from noisy measurements, followed by training the diffusion model using the reconstructed images.
  arXiv  Detail & Related papers  (2024-10-15T03:54:59Z)
• G2D2: Gradient-Guided Discrete Diffusion for Inverse Problem Solving [83.56510119503267]
  This paper presents a novel method for addressing linear inverse problems by leveraging generative models based on discrete diffusion as priors.<n>We employ a star-shaped noise process to mitigate the drawbacks of traditional discrete diffusion models with absorbing states.
  arXiv  Detail & Related papers  (2024-10-09T06:18:25Z)
• Lossy Image Compression with Foundation Diffusion Models [10.407650300093923]
  In this work we formulate the removal of quantization error as a denoising task, using diffusion to recover lost information in the transmitted image latent. Our approach allows us to perform less than 10% of the full diffusion generative process and requires no architectural changes to the diffusion model.
  arXiv  Detail & Related papers  (2024-04-12T16:23:42Z)
• ReNoise: Real Image Inversion Through Iterative Noising [62.96073631599749]
  We introduce an inversion method with a high quality-to-operation ratio, enhancing reconstruction accuracy without increasing the number of operations. We evaluate the performance of our ReNoise technique using various sampling algorithms and models, including recent accelerated diffusion models.
  arXiv  Detail & Related papers  (2024-03-21T17:52:08Z)
• Steerable Conditional Diffusion for Out-of-Distribution Adaptation in Medical Image Reconstruction [75.91471250967703]
  We introduce a novel sampling framework called Steerable Conditional Diffusion.<n>This framework adapts the diffusion model, concurrently with image reconstruction, based solely on the information provided by the available measurement.<n>We achieve substantial enhancements in out-of-distribution performance across diverse imaging modalities.
  arXiv  Detail & Related papers  (2023-08-28T08:47:06Z)
• Low-Light Image Enhancement with Wavelet-based Diffusion Models [50.632343822790006]
  Diffusion models have achieved promising results in image restoration tasks, yet suffer from time-consuming, excessive computational resource consumption, and unstable restoration. We propose a robust and efficient Diffusion-based Low-Light image enhancement approach, dubbed DiffLL.
  arXiv  Detail & Related papers  (2023-06-01T03:08:28Z)
• A Variational Perspective on Solving Inverse Problems with Diffusion Models [101.831766524264]
  Inverse tasks can be formulated as inferring a posterior distribution over data. This is however challenging in diffusion models since the nonlinear and iterative nature of the diffusion process renders the posterior intractable. We propose a variational approach that by design seeks to approximate the true posterior distribution.
  arXiv  Detail & Related papers  (2023-05-07T23:00:47Z)
• Diffusion Models in Vision: A Survey [73.10116197883303]
  A diffusion model is a deep generative model that is based on two stages, a forward diffusion stage and a reverse diffusion stage.<n> Diffusion models are widely appreciated for the quality and diversity of the generated samples, despite their known computational burdens.
  arXiv  Detail & Related papers  (2022-09-10T22:00:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.