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A Residual Diffusion Model for High Perceptual Quality Codec Augmentation

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arxiv 2301.05489 v3 pith:3P3ZVH4C submitted 2023-01-13 cs.CV eess.IV

Noor Fathima Ghouse , Jens Petersen , Auke Wiggers , Tianlin Xu , Guillaume Sautière This is my paper
classification cs.CVeess.IV
keywords codecdiffusionhighmodelsqualityaugmentationcompressionperceptual
verification ladder T0 review T1 audit T2 compute T3 formal
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Diffusion probabilistic models have recently achieved remarkable success in generating high quality image and video data. In this work, we build on this class of generative models and introduce a method for lossy compression of high resolution images. The resulting codec, which we call DIffuson-based Residual Augmentation Codec (DIRAC), is the first neural codec to allow smooth traversal of the rate-distortion-perception tradeoff at test time, while obtaining competitive performance with GAN-based methods in perceptual quality. Furthermore, while sampling from diffusion probabilistic models is notoriously expensive, we show that in the compression setting the number of steps can be drastically reduced.

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Cited by 3 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. SDGIC: A Semantic Disambiguation-Guided Generative Image Compression Method for Ultra-Low Bitrates

    cs.CV 2025-12 conditional novelty 5.0 of 10

    A diffusion-based image codec guided by text, a highly compressed image, and CLIP-derived semantic pseudo-words improves semantic consistency at bitrates below 0.05 bpp.

  3. 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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