JPNeO让JPEG更清晰，还能兼容旧设备。
JPEG Processing Neural Operator for Backward-Compatible Coding
- 用神经算子同时优化编码解码，提升画质。
- 相比传统方法，色彩保留更好，内存和参数更少。
- 无需改标准，即插即用，适合图像压缩研究者。
尽管基于学习的有损压缩算法取得显著进展，但标准化编解码器仍是关键挑战。本文提出下一代JPEG算法——JPEG Processing Neural Operator（JPNeO），在保持与现有JPEG格式完全向后兼容的前提下，通过在编码和解码阶段引入神经算子，提升了色度分量的保留能力并增强了重建保真度。与现有去伪影方法相比，JPNeO在实际应用中实现了更低的内存占用与参数量。我们还通过实证证据验证了高互信息空间存在的假设。总体而言，JPNeO作为无需更改源编码协议的高性能即插即用图像压缩流水线，可直接部署使用。代码已开源：https://github.com/WooKyoungHan/JPNeO。
原文摘要 · Abstract (English)
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.
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