High-Fidelity Image Compression with Score-based Generative Models
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866910358017409024 |
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| author | Hoogeboom, Emiel Agustsson, Eirikur Mentzer, Fabian Versari, Luca Toderici, George Theis, Lucas |
| author_facet | Hoogeboom, Emiel Agustsson, Eirikur Mentzer, Fabian Versari, Luca Toderici, George Theis, Lucas |
| contents | Despite the tremendous success of diffusion generative models in text-to-image generation, replicating this success in the domain of image compression has proven difficult. In this paper, we demonstrate that diffusion can significantly improve perceptual quality at a given bit-rate, outperforming state-of-the-art approaches PO-ELIC and HiFiC as measured by FID score. This is achieved using a simple but theoretically motivated two-stage approach combining an autoencoder targeting MSE followed by a further score-based decoder. However, as we will show, implementation details matter and the optimal design decisions can differ greatly from typical text-to-image models. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2305_18231 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | High-Fidelity Image Compression with Score-based Generative Models Hoogeboom, Emiel Agustsson, Eirikur Mentzer, Fabian Versari, Luca Toderici, George Theis, Lucas Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Despite the tremendous success of diffusion generative models in text-to-image generation, replicating this success in the domain of image compression has proven difficult. In this paper, we demonstrate that diffusion can significantly improve perceptual quality at a given bit-rate, outperforming state-of-the-art approaches PO-ELIC and HiFiC as measured by FID score. This is achieved using a simple but theoretically motivated two-stage approach combining an autoencoder targeting MSE followed by a further score-based decoder. However, as we will show, implementation details matter and the optimal design decisions can differ greatly from typical text-to-image models. |
| title | High-Fidelity Image Compression with Score-based Generative Models |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2305.18231 |