ABSTRACT In the realm of high dynamic range (HDR) imaging, tone mapping (TM) and multi‐exposure fusion (MEF) techniques are essential for achieving visually appealing HDR‐style low dynamic range (LDR) photographs. However, these methods tend to introduce undesirable artefacts, thus requiring robust perceptual quality assessment. This paper proposes a hierarchical framework, dubbed HQaN&A, for automatically evaluating the naturalness and aesthetics attributes of TM/MEF‐processed LDR images. By extracting low‐level naturalness features and high‐level aesthetic attributes, the HQaN&A model simulates the human visual system's hierarchical perception process. Our approach is the first to jointly model naturalness and aesthetic in a hierarchical manner, bridging the gap between low‐level image statistics and high‐level perceptual appeal. Experimental results on benchmark databases demonstrate that our approach outperforms state‐of‐the‐art methods, achieving SRCC = 0.812 and PLCC = 0.813 on the ESPL‐LIVE HDR database. Here, we show that integrating local and regional naturalness with aesthetic evaluations leads to more reliable and comprehensive image quality predictions. This work not only advances the field of image quality assessment but also paves the way for applications in photo editing and automatic aesthetic judgement. Our code will be released at https://github.com/fjl0988/QmN‐A .
Fang et al. (2026) studied this question.