A Novel No-Reference Quality Assessment Model of Tone-Mapped HDR Image

Research on tone mapping operators (TMOs) attracts more attention recently, which can transform high dynamic range (HDR) images to low dynamic range (LDR) images for visualizing them on the common displays. In this paper, we propose a novel no-reference image quality assessment (IQA) model to evaluate the perceptual quality of tone-mapped images (TMIs). Specifically, local phase congruency (LPC) is first computed to evaluate the image sharpness and some statistical characteristics are extracted on the edge maps to measure the halo effect. Meanwhile, TMIs are transformed to opponent color (OC) space to gain the global image chro-maticity and local image contract in the chromatic field. Finally, a regression module is learnt using support vector regression (SVR) to train the mapping function that maps all the features to subjective quality scores. The model shows admirable performance when tested on ESPL-LIVE HDR image database.