Perceptual Quality Assessment of High-Dynamic-Range Image: A Benchmark Dataset and a No Reference Method

High dynamic range (HDR) imaging technology has received increasing attention in recent years, and HDR image quality assessment (IQA) metrics are indispensable during the capturing, processing and displaying of HDR images. However, existing HDR-IQA datasets and methods neglect complex distortions during the HDR image processing schemes, leading to limited generalization performance on practical application. In this work, to facilitate the development of HDR-IQA dataset, we present HDRQAD, a large-scale HDR Quality Assessment Dataset, which possesses diversified distortions during HDR imaging technologies, abundant scenes and considerable quantity. Specifically, the HDRQAD dataset contains 1409 HDR images, which are derived from source scenes with six types of distortions during the HDR imaging schemes. In contrast to existing datasets that contain only compression artifacts, the HDRQAD includes Under-exposure, Over-exposure, Motion blur and Ghosting in HDR images achieved with multi-exposure fusion technology, conversion artifacts in HDR images achieved with single image reconstruction technology and compression artifacts during the transmission of HDR images. Furthermore, during the process of constructing the dataset, we identified three key challenges in HDR-IQA tasks: 1) dynamic range variations, 2) HDR visual artifacts with large overall gap, 3) inter-regional non-uniform image quality. Based on these observations, we propose a new end-to-end network for HDR-IQA tasks, which consists of a Distortion-aware Representation Learning (DRL) module and an Inter-Regional Quality Interaction (IRQI) module. The DRL learns the representations of dynamic range variations and HDR visual artifacts, enhancing the reliability of prior information extraction. The IRQI captures inter-regional quality dependencies with interacting and fusing intermediate distortion features for more accurately predicting image quality. Extensive experiments prove the superiority of proposed HDRQAD and demonstrate that the proposed network achieves state-of-the-art performance. The Dataset and Code will be made publicly available at HDR-IQA-Dataset.