Blind Quality Estimation by Disentangling Perceptual and Noisy Features in High Dynamic Range Images

High dynamic range (HDR) image visual quality assessment in the absence of a reference image is challenging. This research topic has not been adequately studied largely due to the high cost of HDR display devices. Nevertheless, HDR imaging technology has attracted increasing attention, because it provides more realistic content, consistent to what the human visual system perceives. We propose a new no-reference image quality assessment (NR-IQA) model for HDR data based on convolutional neural networks. The proposed model is able to detect visual artifacts, taking into consideration perceptual masking effects, in a distorted HDR image without any reference. The error and perceptual masking values are measured separately, yet sequentially, and then processed by a mixing function to predict the perceived quality of the distorted image. Instead of using simple stimuli and psychovisual experiments, perceptual masking effects are computed from a set of annotated HDR images during our training process. Experimental results demonstrate that our proposed NR-IQA model can predict HDR image quality as accurately as state-of-the-art full-reference IQA methods.