The increasing popularity of high dynamic range (HDR) imaging stems from its ability to faithfully capture luminance levels in natural scenes. However, HDR image quality assessment has been insufficiently addressed. Existing models are mostly designed for low dynamic range (LDR) images, which exhibit poorly correlated with human perception of HDR image quality. To fill this gap, we propose a family of HDR quality metrics by transferring the recent advancements in LDR domain. The key step in our approach is to employ a simple inverse display model to decompose an HDR image into a stack of LDR images with varying exposures. Subsequently, these LDR images are evaluated using state-of-the-art LDR quality metrics. Our family of HDR quality models offer three notable advantages. First, specific exposures (i.e., luminance ranges) can be weighted to emphasize their assessment when calculating the overall quality score. Second, our HDR quality metrics directly inherit the capabilities of their base LDR quality models in assessing LDR images. Third, our metrics do not rely on human perceptual data of HDR image quality for re-calibration. Experiments conducted on four human-rated HDR image quality datasets indicate that our HDR quality metrics consistently outperform existing methods, including the HDR-VDP family. Furthermore, we demonstrate the promise of our models in the perceptual optimization of HDR novel view synthesis.

翻译：高动态范围（HDR）成像因其能忠实捕捉自然场景中的亮度水平而日益流行。然而，HDR图像质量评估的研究尚未充分展开。现有模型主要针对低动态范围（LDR）图像设计，与人类对HDR图像质量的感知相关性较差。为填补这一空白，我们通过迁移LDR领域的最新进展，提出了一系列HDR质量指标。该方法的关键步骤是利用一个简单的逆显示模型，将HDR图像分解为一组具有不同曝光度的LDR图像堆栈。随后，这些LDR图像采用最先进的LDR质量指标进行评估。我们的HDR质量模型系列具有三个显著优势：首先，在计算整体质量分数时，可对特定曝光（即亮度范围）进行加权以强调其评估；其次，HDR质量指标直接继承了其基础LDR质量模型在评估LDR图像时的能力；第三，我们的指标无需依赖HDR图像质量的人类感知数据重新校准。在四个经人类评分的HDR图像质量数据集上进行的实验表明，我们的HDR质量指标始终优于现有方法，包括HDR-VDP系列。此外，我们展示了这些模型在HDR新视角合成感知优化中的潜力。