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JPEG Processing Neural Operator for Backward-Compatible Coding

arXiv 2507.23521 ↗First posted 31 Jul 2025eess.IV cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 30 of its 49 references that are in the map. First posted on arXiv 31 Jul 2025.

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References · 30

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18 more
  1. 2023WIRE: Wavelet Implicit Neural Representationscited by 220
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  18. 2022Practical Learned Lossless JPEG Recompression with Multi-Level Cross-Channel Entropy Model in the DCT Domaincited by 4

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