JPEG Processing Neural Operator for Backward-Compatible Coding

Woo Kyoung Han1*, Youngjun Lee1*, Byeonghun Lee1,
Sang Hyun Park2, Sunghoon Im2†, Kyong Hwan Jin1†
Korea University1, DGIST2
ICCV 2025
* Equal Contribution, † Corresponding Authors
Code arXiv

Overview of the proposed method. . Our method enables flexible switching between the conventional JPEG encoder and decoder as needed. Our approach ensures interoperability, allowing conventional JPEG-encoded files to be decoded with a conventional JPEG decoder and existing JPEG files to be decoded using our method

Abstract

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.