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    Diffusion models’ role in image compression

    A coauthor says the article reviews generative compression and “near-optimal” tradeoffs between data rate, image distortion and realism.

    SM
    1 Source, 17d ago, first seen 17d ago

    TLDR

    Diffusion models can do more than generate images: they can save us from transmitting all their detail, according to a coauthor of an IEEE BITS article. The coauthor describes the review as covering generative compression and near-optimal tradeoffs among how much data is sent, image distortion and realism, and credits Yibo Yang with leading the work.

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    1 Source, first seen 17d ago

    Combined views

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    1 Source, first seen 17d ago

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    1 Source

    @StephanMandtDiffusion models can do more than generate images: they can save us from transmitting all their detail. Our IEEE BITS article reviews generative compression and near-optimal rate–distortion–realism tradeoffs. Led by @YiboYang, who did the lion’s share. https://arxiv.org/pdf/2601.18932

    1 Source

    @StephanMandtDiffusion models can do more than generate images: they can save us from transmitting all their detail. Our IEEE BITS article reviews generative compression and near-optimal rate–distortion–realism tradeoffs. Led by @YiboYang, who did the lion’s share. https://arxiv.org/pdf/2601.18932