Related papers: JPEG Processing Neural Operator for Backward-Compatible Coding

JPEG Processing Neural Operator for Backward-Compatible Coding

URL: http://arxiv.org/abs/2507.23521v1
Date: Thu, 31 Jul 2025 13:04:55 GMT
Title: JPEG Processing Neural Operator for Backward-Compatible Coding
Authors: Woo Kyoung Han, Yongjun Lee, Byeonghun Lee, Sang Hyun Park, Sunghoon Im, Kyong Hwan Jin,
Abstract summary: We present a next-generation JPEG algorithm that maintains full backward compatibility with the current JPEG format.<n>Our JPNeO improves chroma component preservation and enhances reconstruction fidelity compared to existing artifact removal methods.
Score: 17.84579881687547
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
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. Our source code is available at https://github.com/WooKyoungHan/JPNeO.

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