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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.14130 |
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| _version_ | 1866914266475397120 |
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| author | Aczel, Till Jenny, David F. Bührer, Simon Plesner, Andreas Di Maio, Antonio Wattenhofer, Roger |
| author_facet | Aczel, Till Jenny, David F. Bührer, Simon Plesner, Andreas Di Maio, Antonio Wattenhofer, Roger |
| contents | Neural image codecs achieve higher compression ratios than traditional hand-crafted methods such as PNG or JPEG-XL, but often incur substantial computational overhead, limiting their deployment on energy-constrained devices such as smartphones, cameras, and drones. We propose Grayscale Image Compression with Differentiable Logic Circuits (GIC-DLC), a hardware-aware codec where we train lookup tables to combine the flexibility of neural networks with the efficiency of Boolean operations. Experiments on grayscale benchmark datasets show that GIC-DLC outperforms traditional codecs in compression efficiency while allowing substantial reductions in energy consumption and latency. These results demonstrate that learned compression can be hardware-friendly, offering a promising direction for low-power image compression on edge devices. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_14130 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | GIC-DLC: Differentiable Logic Circuits for Hardware-Friendly Grayscale Image Compression Aczel, Till Jenny, David F. Bührer, Simon Plesner, Andreas Di Maio, Antonio Wattenhofer, Roger Computer Vision and Pattern Recognition Neural image codecs achieve higher compression ratios than traditional hand-crafted methods such as PNG or JPEG-XL, but often incur substantial computational overhead, limiting their deployment on energy-constrained devices such as smartphones, cameras, and drones. We propose Grayscale Image Compression with Differentiable Logic Circuits (GIC-DLC), a hardware-aware codec where we train lookup tables to combine the flexibility of neural networks with the efficiency of Boolean operations. Experiments on grayscale benchmark datasets show that GIC-DLC outperforms traditional codecs in compression efficiency while allowing substantial reductions in energy consumption and latency. These results demonstrate that learned compression can be hardware-friendly, offering a promising direction for low-power image compression on edge devices. |
| title | GIC-DLC: Differentiable Logic Circuits for Hardware-Friendly Grayscale Image Compression |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.14130 |