Perceptual Tone Mapping Model for High Dynamic Range Imaging
Imran Mehmood Xinye Shi M. Usman Khan Ming Ronnier Luo
One of the key challenges in tone mapping is to preserve the perceptual
quality of high dynamic range (HDR) images when mapping them to standard
dynamic range (SDR) displays. Traditional tone mapping operators (TMOs)
compress the luminance of HDR images without considering the surround and
display conditions emanating into suboptimal results. Current research
addresses this challenge by incorporating perceptual color appearance
attributes. In this work, we propose a TMO (TMOz) that leverages CIECAM16
perceptual attributes, i.e., brightness, colorfulness, and hue. TMOz accounts
for the effects of both the surround and the display conditions to achieve more
optimal colorfulness reproduction. The perceptual brightness is compressed, and
the perceptual color scales, i.e., colorfulness and hue are derived from HDR
images by employing CIECAM16 color adaptation equations. A psychophysical
experiment was conducted to automate the brightness compression parameter. The
model employs fully automatic and adaptive approach, obviating the requirement
for manual parameter selection. TMOz was evaluated in terms of contrast,
colorfulness and overall image quality. The objective and subjective evaluation
methods revealed that the proposed model outperformed the state-of-the-art
TMOs.