High dynamic range (HDR) imaging has gained increasing popularity for its
ability to faithfully reproduce the luminance levels in natural scenes.
Accordingly, HDR image quality assessment (IQA) is crucial but has been
superficially treated. The majority of existing IQA models are developed for
and calibrated against low dynamic range (LDR) images, which have been shown to
be poorly correlated with human perception of HDR image quality. In this work,
we propose a family of HDR IQA models by transferring the recent advances in
LDR IQA. The key step in our approach is to specify a simple inverse display
model that decomposes an HDR image to a set of LDR images with different
exposures, which will be assessed by existing LDR quality models. The local
quality scores of each exposure are then aggregated with the help of a simple
well-exposedness measure into a global quality score for each exposure, which
will be further weighted across exposures to obtain the overall quality score.
When assessing LDR images, the proposed HDR quality models reduce gracefully to
the original LDR ones with the same performance. Experiments on four
human-rated HDR image datasets demonstrate that our HDR quality models are
consistently better than existing IQA methods, including the HDR-VDP family.
Moreover, we demonstrate their strengths in perceptual optimization of HDR
novel view synthesis.

提出了一种新的高动态范围图像质量评估模型，通过逆显示模型将 HDR 图像分解成一组具有不同曝光的 LDR 图像，然后使用现有的 LDR 图像质量模型对每个曝光进行评估，并根据曝光之间的全局质量评分来获得整体质量评分。该方法在评估 LDR 图像时具有相同的性能，并在人感知优化 HDR 新视角合成方面表现出了优势。