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Practical Lossless Recompression of JPEG Images Using Transform Domain Prediction

Long Chen,Wenhua Hu,Rui Hao,Junwei Zhou,Jianwen Xiang

Published 2025 in Asia-Pacific Software Engineering Conference

ABSTRACT

The inefficiency of the decades-old JPEG standard imposes a significant maintenance and cost burden on largescale software ecosystems that handle trillions of legacy files. Current solutions are ineffective, as modern codecs are incompatible with JPEG’s unique artifacts, while existing recompression tools rely on ad-hoc, handcrafted algorithms with fundamental performance limitations. To address this challenge systematically, we introduce PLLR, a reusable software framework built upon a novel design paradigm: learned information decomposition. Instead of manual rule-making, our framework automates the process of identifying and separating redundancies. It employs a Variational Autoencoder to learn a global, probabilistic model of the image content, effectively decoupling the predictable signal components from a highly sparse, low-entropy residual. By isolating this essential information, the subsequent entropy coding stage becomes significantly more efficient. Our implementation of this framework establishes a new state-of-the-art, achieving a fully lossless file size reduction of 31.54% on the Kodak dataset. This result validates the superiority of our learned decomposition framework for legacy data compression and offers a practical pathway to reduce the immense economic and environmental costs associated with large-scale data systems.

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