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.

Giuseppe Valenzise completed a master degree and a Ph.D. in Information Technology at the Politecnico di Milano, Italy, in 2007 and 2011, respectively. In 2012, he joined the French Centre National de la Recherche Scientifique (CNRS) as a permanent researcher, first at the Laboratoire Traitement et Communication de l'Information (LTCI) Telecom Paristech, and from 2016 at the Laboratoire des Signaux et Systmes (L2S), CentraleSupelec Université Paris-Sud. His research interests span different fields of image and video processing, including high dynamic range imaging, video quality assessment, single and multi-view video coding, applications of machine learning to image and video analysis. He is co-author of more than 70 research publications and of several award-winning papers. He is the recipient of the EURASIP Early Career Award 2018. Dr. Valenzise serves as Associate Editor for IEEE Transactions on Circuits and Systems for Video Technology, as well as for Elsevier Signal Processing: Image communication. He is a member of the MMSP and IVMSP technical committees of the IEEE Signal Processing Society for the term 2018-2020, as well as a member of the Special Area Team on Visual Information Processing of EURASIP.