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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 · ORCID
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 11 Pith papers

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

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    ResULIC combines residual text captions, optimized prompts, and bitrate-aware diffusion steps to reach state-of-the-art perceptual quality at ultra-low bitrates.

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    A screen-and-natural-image codec that losslessly encodes OCR text and uses a diffusion renderer with three levels of conditioning achieves state-of-the-art perceptual quality and high text accuracy at 0.005 to 0.05 bpp.

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    An ROI-weighted latent feature space with conditional diffusion reconstruction is claimed to reduce perceptual error by 43.3% versus DeepJSCC in semantic image communication.

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