
The existing tone mapping operators (TMOs), compress either the high dynamic range (HDR) image luminance or RGB channels and assume uniform adaptation conditions, contrary to human vision that adapts colorfulness under varying adaptation luminance conditions. One of the challenges in tone mapping is maintaining perceptual consistency of both lightness and colorfulness under varying adaptation luminance. Unlike traditional approaches, this work proposes CIECAM16 lightness based, spatially adaptive tone mapping and allows colorfulness according to local adaptation luminance. Furthermore, it uses spatial white point instead of a global one aligning the human perceptual phenomenon. The paper further analyzes the performance of the proposed TMO across various spatial conditions, demonstrating that it preserves local contrast and maintains detail in both highlight and shadow regions while adaptively regulating colorfulness under various adaptation conditions. Hence, this adaptive approach for HDR to standard dynamic range (SDR) mapping offers perceptually faithful representation.
Imran Mehmood, Brian Deegan, Ming Ronnier Luo, "Lightness-based Spatially Adaptive Tone and Colorfulness Reproduction for HDR Imaging" in Electronic Imaging, 2026, pp 227-1 - 227-7, https://doi.org/10.2352/EI.2026.38.10.HVEI-227