Generative Preprocessing for Image Compression with Pre-trained Diffusion Models

• URL: http://arxiv.org/abs/2512.15270v1
• Date: Wed, 17 Dec 2025 10:22:11 GMT
• Title: Generative Preprocessing for Image Compression with Pre-trained Diffusion Models
• Authors: Mengxi Guo, Shijie Zhao, Junlin Li, Li Zhang,
• Abstract summary: This work pioneers a shift towards Rate-Perception (R-P) optimization by, for the first time, adapting a large-scale pre-trained diffusion model for compression preprocessing.<n> Experiments show substantial R-P gains, achieving up to a 30.13% BD-rate reduction in DISTS on the Kodak dataset and delivering superior subjective visual quality.
• Score: 18.470327978505065
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Preprocessing is a well-established technique for optimizing compression, yet existing methods are predominantly Rate-Distortion (R-D) optimized and constrained by pixel-level fidelity. This work pioneers a shift towards Rate-Perception (R-P) optimization by, for the first time, adapting a large-scale pre-trained diffusion model for compression preprocessing. We propose a two-stage framework: first, we distill the multi-step Stable Diffusion 2.1 into a compact, one-step image-to-image model using Consistent Score Identity Distillation (CiD). Second, we perform a parameter-efficient fine-tuning of the distilled model's attention modules, guided by a Rate-Perception loss and a differentiable codec surrogate. Our method seamlessly integrates with standard codecs without any modification and leverages the model's powerful generative priors to enhance texture and mitigate artifacts. Experiments show substantial R-P gains, achieving up to a 30.13% BD-rate reduction in DISTS on the Kodak dataset and delivering superior subjective visual quality.

Related papers

• Turbo-DDCM: Fast and Flexible Zero-Shot Diffusion-Based Image Compression [54.1069581766925]
  This paper presents an efficient zero-shot diffusion-based compression method that runs substantially faster than existing methods.<n>Our method builds upon the recently proposed Denoising Diffusion Codebook Models (DDCMs) compression scheme.<n>We introduce two flexible variants of Turbo-DDCM, a priority-aware variant that prioritizes user-specified regions and a distortion-controlled variant that compresses an image based on a target PSNR rather than a target BPP.
  arXiv  Detail & Related papers  (2025-11-09T15:41:27Z)
• Generative Image Coding with Diffusion Prior [3.127638190046881]
  We propose a novel generative coding framework leveraging diffusion priors to enhance compression performance at lows.<n>We show that our method outperforms existing methods in visual fidelity across lows encoder, (2) improves compression performance by up to 79% over H.266/VVC, and (3) offers an efficient solution for AI-generated content while being adaptable to broader content types.
  arXiv  Detail & Related papers  (2025-09-17T07:32:15Z)
• SODiff: Semantic-Oriented Diffusion Model for JPEG Compression Artifacts Removal [50.90827365790281]
  SODiff is a semantic-oriented one-step diffusion model for JPEG artifacts removal.<n>Our core idea is that effective restoration hinges on providing semantic-oriented guidance to the pre-trained diffusion model.<n>SAIPE extracts rich features from low-quality (LQ) images and projects them into an embedding space semantically aligned with that of the text encoder.
  arXiv  Detail & Related papers  (2025-08-10T13:48:07Z)
• Steering One-Step Diffusion Model with Fidelity-Rich Decoder for Fast Image Compression [36.10674664089876]
  SODEC is a novel single-step diffusion-based image compression model.<n>It improves fidelity resulting from over-reliance on generative priors.<n>It significantly outperforms existing methods, achieving superior rate-distortion-perception performance.
  arXiv  Detail & Related papers  (2025-08-07T02:24:03Z)
• 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)
• Higher fidelity perceptual image and video compression with a latent conditioned residual denoising diffusion model [55.2480439325792]
  We propose a hybrid compression scheme optimized for perceptual quality, extending the approach of the CDC model with a decoder network.<n>We achieve up to +2dB PSNR fidelity improvements while maintaining comparable LPIPS and FID perceptual scores when compared with CDC.
  arXiv  Detail & Related papers  (2025-05-19T14:13:14Z)
• One-Step Diffusion Model for Image Motion-Deblurring [85.76149042561507]
  We propose a one-step diffusion model for deblurring (OSDD), a novel framework that reduces the denoising process to a single step.<n>To tackle fidelity loss in diffusion models, we introduce an enhanced variational autoencoder (eVAE), which improves structural restoration.<n>Our method achieves strong performance on both full and no-reference metrics.
  arXiv  Detail & Related papers  (2025-03-09T09:39:57Z)
• Correcting Diffusion-Based Perceptual Image Compression with Privileged End-to-End Decoder [49.01721042973929]
  This paper presents a diffusion-based image compression method that employs a privileged end-to-end decoder model as correction. Experiments demonstrate the superiority of our method in both distortion and perception compared with previous perceptual compression methods.
  arXiv  Detail & Related papers  (2024-04-07T10:57:54Z)
• Hierarchical Integration Diffusion Model for Realistic Image Deblurring [71.76410266003917]
  Diffusion models (DMs) have been introduced in image deblurring and exhibited promising performance. We propose the Hierarchical Integration Diffusion Model (HI-Diff), for realistic image deblurring. Experiments on synthetic and real-world blur datasets demonstrate that our HI-Diff outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-22T12:18:20Z)
• Lossy Image Compression with Conditional Diffusion Models [25.158390422252097]
  This paper outlines an end-to-end optimized lossy image compression framework using diffusion generative models. In contrast to VAE-based neural compression, where the (mean) decoder is a deterministic neural network, our decoder is a conditional diffusion model. Our approach yields stronger reported FID scores than the GAN-based model, while also yielding competitive performance with VAE-based models in several distortion metrics.
  arXiv  Detail & Related papers  (2022-09-14T21:53:27Z)
• Channel-wise Autoregressive Entropy Models for Learned Image Compression [8.486483425885291]
  In learning-based approaches to image compression, codecs are developed by optimizing a computational model to minimize a rate-distortion objective. We introduce two enhancements, channel-conditioning and latent residual prediction, that lead to network architectures with better rate-distortion performance. At low bit rates, where the improvements are most effective, our model saves up to 18% over the baseline and outperforms hand-engineered codecs like BPG by up to 25%.
  arXiv  Detail & Related papers  (2020-07-17T03:33:53Z)

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.