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High-Fidelity Generative Image Compression

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arxiv 2006.09965 v3 pith:BPUGDT7S submitted 2020-06-17 eess.IV cs.CVcs.LG

Fabian Mentzer , George Toderici , Michael Tschannen , Eirikur Agustsson This is my paper
classification eess.IVcs.CVcs.LG
keywords compressiongenerativeapproachobtainperceptualpreviousadversarialapplied
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We extensively study how to combine Generative Adversarial Networks and learned compression to obtain a state-of-the-art generative lossy compression system. In particular, we investigate normalization layers, generator and discriminator architectures, training strategies, as well as perceptual losses. In contrast to previous work, i) we obtain visually pleasing reconstructions that are perceptually similar to the input, ii) we operate in a broad range of bitrates, and iii) our approach can be applied to high-resolution images. We bridge the gap between rate-distortion-perception theory and practice by evaluating our approach both quantitatively with various perceptual metrics, and with a user study. The study shows that our method is preferred to previous approaches even if they use more than 2x the bitrate.

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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. Drop-In Perceptual Optimization for 3D Gaussian Splatting

    cs.CV 2026-03 accept novelty 6.5 of 10

    WD-R, a lightly regularized Wasserstein Distortion loss, is preferred by humans 2.3× over the original 3DGS loss and 1.5× over Perceptual-GS while matching or reducing splat count and generalizing to other 3DGS framew...

  2. Exploring Autoregressive Vision Foundation Models for Image Compression

    eess.IV 2025-09 conditional novelty 6.0 of 10

    Pretrained autoregressive vision foundation models can be repurposed directly as image entropy coders, delivering competitive perceptual quality at very low bitrates with no fine-tuning.

  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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