DCDiff: Enhancing JPEG Compression via Diffusion-based DC Coefficients Estimation

JPEG is the most widely-used image compression method on low-cost cameras which cannot support learning-based compressors. One promising approach to enhance JPEG aims to drop DC coefficients at the cameras' ends (without extra computation) and reconstruct those DC coefficients after receiving t...

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Veröffentlicht in:2025 62nd ACM/IEEE Design Automation Conference (DAC) S. 1 - 7
Hauptverfasser: Zhang, Ziyuan, Qiu, Han, Zhang, Tianwei, Chen, Bin, Zhang, Chao
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 22.06.2025
Schlagworte:
Diffusion model
Estimation
Image coding
Image compression
Image reconstruction
JPEG
Performance evaluation
Program processors
Propagation losses
Statistical analysis
Statistical distributions
Throughput
Transform coding
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Zusammenfassung:JPEG is the most widely-used image compression method on low-cost cameras which cannot support learning-based compressors. One promising approach to enhance JPEG aims to drop DC coefficients at the cameras' ends (without extra computation) and reconstruct those DC coefficients after receiving them. They all face the challenge that their DC reconstruction relies on a statistical property, which will cause deviationintroduced errors and propagate. In this paper, we propose DCDiff, a novel end-to-end DC estimation method to tackle the above challenge. Instead of using statistical methods to recover DC coefficients and then fix errors, we directly leverage a generative model to estimate DC coefficients in an end-to-end manner. In the meantime, we generate masks to correct certain image locations that do not satisfy the statistical distribution to suppress error propagation. Extensive experiments show that DCDiff not only outperforms all baselines on compression performance but also introduces a tiny impact on downstream tasks and is fully compatible with 2 typical low-cost processors with JPEG support.
DOI:10.1109/DAC63849.2025.11132562