Learned Image Compression with Dictionary-based Entropy Model
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866910944603406336 |
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| author | Lu, Jingbo Zhang, Leheng Zhou, Xingyu Li, Mu Li, Wen Gu, Shuhang |
| author_facet | Lu, Jingbo Zhang, Leheng Zhou, Xingyu Li, Mu Li, Wen Gu, Shuhang |
| contents | Learned image compression methods have attracted great research interest and exhibited superior rate-distortion performance to the best classical image compression standards of the present. The entropy model plays a key role in learned image compression, which estimates the probability distribution of the latent representation for further entropy coding. Most existing methods employed hyper-prior and auto-regressive architectures to form their entropy models. However, they only aimed to explore the internal dependencies of latent representation while neglecting the importance of extracting prior from training data. In this work, we propose a novel entropy model named Dictionary-based Cross Attention Entropy model, which introduces a learnable dictionary to summarize the typical structures occurring in the training dataset to enhance the entropy model. Extensive experimental results have demonstrated that the proposed model strikes a better balance between performance and latency, achieving state-of-the-art results on various benchmark datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_00496 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learned Image Compression with Dictionary-based Entropy Model Lu, Jingbo Zhang, Leheng Zhou, Xingyu Li, Mu Li, Wen Gu, Shuhang Computer Vision and Pattern Recognition Learned image compression methods have attracted great research interest and exhibited superior rate-distortion performance to the best classical image compression standards of the present. The entropy model plays a key role in learned image compression, which estimates the probability distribution of the latent representation for further entropy coding. Most existing methods employed hyper-prior and auto-regressive architectures to form their entropy models. However, they only aimed to explore the internal dependencies of latent representation while neglecting the importance of extracting prior from training data. In this work, we propose a novel entropy model named Dictionary-based Cross Attention Entropy model, which introduces a learnable dictionary to summarize the typical structures occurring in the training dataset to enhance the entropy model. Extensive experimental results have demonstrated that the proposed model strikes a better balance between performance and latency, achieving state-of-the-art results on various benchmark datasets. |
| title | Learned Image Compression with Dictionary-based Entropy Model |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.00496 |