JPEG Processing Neural Operator for Backward-Compatible Coding
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909713753440256 |
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| author | Han, Woo Kyoung Lee, Yongjun Lee, Byeonghun Park, Sang Hyun Im, Sunghoon Jin, Kyong Hwan |
| author_facet | Han, Woo Kyoung Lee, Yongjun Lee, Byeonghun Park, Sang Hyun Im, Sunghoon Jin, Kyong Hwan |
| contents | Despite significant advances in learning-based lossy compression algorithms, standardizing codecs remains a critical challenge. In this paper, we present the JPEG Processing Neural Operator (JPNeO), a next-generation JPEG algorithm that maintains full backward compatibility with the current JPEG format. Our JPNeO improves chroma component preservation and enhances reconstruction fidelity compared to existing artifact removal methods by incorporating neural operators in both the encoding and decoding stages. JPNeO achieves practical benefits in terms of reduced memory usage and parameter count. We further validate our hypothesis about the existence of a space with high mutual information through empirical evidence. In summary, the JPNeO functions as a high-performance out-of-the-box image compression pipeline without changing source coding's protocol. Our source code is available at https://github.com/WooKyoungHan/JPNeO. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_23521 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | JPEG Processing Neural Operator for Backward-Compatible Coding Han, Woo Kyoung Lee, Yongjun Lee, Byeonghun Park, Sang Hyun Im, Sunghoon Jin, Kyong Hwan Image and Video Processing Computer Vision and Pattern Recognition Despite significant advances in learning-based lossy compression algorithms, standardizing codecs remains a critical challenge. In this paper, we present the JPEG Processing Neural Operator (JPNeO), a next-generation JPEG algorithm that maintains full backward compatibility with the current JPEG format. Our JPNeO improves chroma component preservation and enhances reconstruction fidelity compared to existing artifact removal methods by incorporating neural operators in both the encoding and decoding stages. JPNeO achieves practical benefits in terms of reduced memory usage and parameter count. We further validate our hypothesis about the existence of a space with high mutual information through empirical evidence. In summary, the JPNeO functions as a high-performance out-of-the-box image compression pipeline without changing source coding's protocol. Our source code is available at https://github.com/WooKyoungHan/JPNeO. |
| title | JPEG Processing Neural Operator for Backward-Compatible Coding |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.23521 |