Abstract
High Dynamic Range (HDR) images, with their expanded range of brightness and color, provide a far more realistic and immersive viewing experience compared to Low Dynamic Range (LDR) images. However, the significant increase in peak luminance and contrast inherent in HDR images often accentuates artifacts, thus limiting the effectiveness of traditional LDR-based image quality assessment (IQA) algorithms when applied to HDR content. To address this, we propose a novel blind IQA method tailored specifically for HDR images, which incorporates both the perception and inference processes of the human visual system (HVS). Our approach begins with multi-scale Retinex decomposition to generate reflectance maps with varying sensitivity, followed by the calculation of gradient similarities from these maps to model the perception process. Deep feature maps are then extracted from the last pooling layer of a pretrained VGG16 network to capture inference characteristics. These gradient similarity maps and deep feature maps are subsequently aggregated for quality prediction using support vector regression (SVR). Experimental results demonstrate that the proposed method achieves outstanding performance, outperforming other state-of-the-art HDR IQA metrics.