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.

翻译：神经图像编解码器相比传统手工设计方法（如PNG或JPEG-XL）实现了更高的压缩比，但通常伴随着显著的计算开销，限制了其在智能手机、相机和无人机等能量受限设备上的部署。我们提出了基于可微分逻辑电路的灰度图像压缩方法（GIC-DLC），这是一种硬件感知的编解码器，通过训练查找表将神经网络的灵活性与布尔运算的高效性相结合。在灰度基准数据集上的实验表明，GIC-DLC在压缩效率上优于传统编解码器，同时能显著降低能耗与延迟。这些结果表明，学习型压缩方法可以实现硬件友好性，为边缘设备上的低功耗图像压缩提供了有前景的发展方向。