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High-Fidelity Image Compression with Score-based Generative Models

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arxiv 2305.18231 v3 pith:UA4V25FP submitted 2023-05-26 eess.IV cs.CVcs.LGstat.ML

Emiel Hoogeboom , Eirikur Agustsson , Fabian Mentzer , Luca Versari , George Toderici , Lucas Theis This is my paper
classification eess.IVcs.CVcs.LGstat.ML
keywords modelscompressiondiffusiongenerativeimagescore-basedsuccesstext-to-image
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Despite the tremendous success of diffusion generative models in text-to-image generation, replicating this success in the domain of image compression has proven difficult. In this paper, we demonstrate that diffusion can significantly improve perceptual quality at a given bit-rate, outperforming state-of-the-art approaches PO-ELIC and HiFiC as measured by FID score. This is achieved using a simple but theoretically motivated two-stage approach combining an autoencoder targeting MSE followed by a further score-based decoder. However, as we will show, implementation details matter and the optimal design decisions can differ greatly from typical text-to-image models.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Next-Frame Decoding for Ultra-Low-Bitrate Image Compression with Video Diffusion Priors

    cs.CV 2026-03 conditional novelty 6.5 of 10

    Ultra-low-bitrate image decoding is cast as one-step next-frame prediction from a compact anchor using adapted video diffusion priors, yielding large perceptual bitrate savings versus DiffC.

  2. SSDD: Single-Step Diffusion Decoder for Efficient Image Tokenization

    cs.CV 2025-10 conditional novelty 6.0 of 10

    A GAN-free, single-step distilled diffusion decoder that reconstructs images from latent codes with better perceptual FID than KL-VAE at higher throughput.

  3. StableCodec: Taming One-Step Diffusion for Extreme Image Compression

    eess.IV 2025-06 conditional novelty 6.0 of 10

    A one-step diffusion codec that compresses noisy latents at 64x and decodes with a single denoising step, setting state-of-the-art FID, KID, and DISTS at ultra-low bitrates.

  4. Fast Training-free Perceptual Image Compression

    eess.IV 2025-06 conditional novelty 5.0 of 10

    A noise-then-denoise decoder with a pre-trained diffusion model turns any existing codec into a fast, training-free perceptual codec with a KL-divergence guarantee and 0.1-10s decoding.

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