The overall performance comparison results.

In the process of multi-exposure image fusion (MEF), the appearance of various distortions will inevitably cause the deterioration of visual quality. It is essential to predict the visual quality of MEF images. In this work, a novel blind image quality assessment (IQA) method is proposed for MEF images considering the detail, structure, and color characteristics. Specifically, to better perceive the detail and structure distortion, based on the joint bilateral filtering, the MEF image is decomposed into two layers (i.e., the energy layer and the structure layer). Obviously, this is a symmetric process that the two decomposition results can independently and almost completely describe the information of MEF images. As the former layer contains rich intensity information and the latter captures some image structures, some energy-related and structure-related features are extracted from these two layers to perceive the detail and structure distortion phenomena. Besides, some color-related features are also obtained to present the color degradation which are combined with the above energy-related and structure-related features for quality regression. Experimental results on the public MEF image database demonstrate that the proposed method achieves higher performance than the state-of-the-art quality assessment ones.

在多曝光图像融合（multi-exposure image fusion, MEF）过程中，各类失真的出现不可避免地会导致视觉质量下降，因此对MEF图像的视觉质量进行预测具有重要意义。本研究针对MEF图像，提出一种兼顾细节、结构与色彩特征的新型盲图像质量评估（blind image quality assessment, IQA）方法。具体而言，为更好地感知细节与结构失真，本方法基于联合双边滤波，将MEF图像分解为能量层与结构层两个分量。该分解过程具有对称性，两个分解结果可独立且近乎完整地描述MEF图像的信息：前者包含丰富的强度信息，后者则捕捉图像的结构特征。据此，我们从这两个分量中提取与能量、结构相关的特征，以感知细节与结构失真现象。此外，本方法还提取与色彩相关的特征以表征色彩退化问题，并将其与前述能量、结构特征相结合，用于最终的质量回归预测。在公开MEF图像数据库上的实验结果表明，所提方法的性能优于当前主流的先进质量评估方法。