Advanced driver assistance systems (ADAS) relying on multiple cameras are
increasingly prevalent in vehicle technology. Yet, conventional imaging sensors
struggle to capture clear images in conditions with intense illumination
contrast, such as tunnel exits, due to their limited dynamic range. Introducing
high dynamic range (HDR) sensors addresses this issue. However, the process of
converting HDR content to a displayable range via tone mapping often leads to
inefficient computations, when performed directly on pixel data. In this paper,
we focus on HDR image tone mapping using a lightweight neural network applied
on image histogram data. Our proposed TinyML-based global tone mapping method,
termed as TGTM, operates at 9,000 FLOPS per RGB image of any resolution.
Additionally, TGTM offers a generic approach that can be incorporated to any
classical tone mapping method. Experimental results demonstrate that TGTM
outperforms state-of-the-art methods on real HDR camera images by up to 5.85 dB
higher PSNR with orders of magnitude less computations.

本研究论文介绍了一种基于小型机器学习的全局色调映射方法（TGTM），该方法在任意分辨率下的每个 RGB 图像上以 9,000 FLOPS 的计算速度操作，通过应用于图像直方图数据，实现了 HDR 图像的色调映射，结果表明 TGTM 在实际 HDR 相机图像上相对于现有方法具有更高的峰值信噪比（PSNR），而计算量则较低。