JPEG Processing Neural Operator for Backward-Compatible Coding
This addresses the problem of backward compatibility in image compression for users and developers, though it is incremental as it builds on existing JPEG standards.
The paper tackles the challenge of standardizing learning-based lossy compression by introducing JPNeO, a JPEG-compatible algorithm that improves chroma preservation and reconstruction fidelity, achieving reduced memory usage and parameter count.
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.