  High dynamic range (HDR) rendering has the ability to faithfully reproduce
the wide luminance ranges in natural scenes, but how to accurately assess the
rendering quality is relatively underexplored. Existing quality models are
mostly designed for low dynamic range (LDR) images, and do not align well with
human perception of HDR image quality. To fill this gap, we propose a family of
HDR quality metrics, in which the key step is employing a simple inverse
display model to decompose an HDR image into a stack of LDR images with varying
exposures. Subsequently, these decomposed images are assessed through
well-established LDR quality metrics. Our HDR quality models present three
distinct benefits. First, they directly inherit the recent advancements of LDR
quality metrics. Second, they do not rely on human perceptual data of HDR image
quality for re-calibration. Third, they facilitate the alignment and
prioritization of specific luminance ranges for more accurate and detailed
quality assessment. Experimental results show that our HDR quality metrics
consistently outperform existing models in terms of quality assessment on four
HDR image quality datasets and perceptual optimization of HDR novel view
synthesis.
