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Text + Sketch: Image Compression at Ultra Low Rates

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arxiv 2307.01944 v1 pith:DOSTW36W submitted 2023-07-04 cs.LG cs.CVcs.ITmath.IT

Eric Lei , Yiğit Berkay Uslu , Hamed Hassani , Shirin Saeedi Bidokhti This is my paper
classification cs.LGcs.CVcs.ITmath.IT
keywords modelscompressiontextdescriptionsgenerateimagepre-trainedtraining
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Recent advances in text-to-image generative models provide the ability to generate high-quality images from short text descriptions. These foundation models, when pre-trained on billion-scale datasets, are effective for various downstream tasks with little or no further training. A natural question to ask is how such models may be adapted for image compression. We investigate several techniques in which the pre-trained models can be directly used to implement compression schemes targeting novel low rate regimes. We show how text descriptions can be used in conjunction with side information to generate high-fidelity reconstructions that preserve both semantics and spatial structure of the original. We demonstrate that at very low bit-rates, our method can significantly improve upon learned compressors in terms of perceptual and semantic fidelity, despite no end-to-end training.

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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. 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.

  3. Generative Image Compression by Estimating Gradients of the Rate-variable Feature Distribution

    eess.IV 2025-05 conditional novelty 6.0 of 10

    A single rate-variable generative compression model treats quantization as a forward corruption and reverses it with a two-step denoiser, outperforming prior generative codecs on perceptual quality benchmarks.

  4. 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.

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