  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.
