Just Noticeable Difference Estimation for Screen Content Images: A Content Uncertainty-guided Approach

Just-noticeable-difference (JND) effectively describes the threshold of the human visual system (HVS) perceiving visual signal changes, which reflects the visual redundancy. Researches indicate that the information distribution characteristics of images play an important role in the information inference of HVS. Inspired by this, we present a content uncertainty-guided JND estimation approach for screen content images (SCIs). Specifically, we divide SCIs into certain content and uncertain content according to content uncertainty modeling. Based on the analysis of content characteristics, we apply oblique masking (OM) and contrast masking (CM) in uncertain content, and consider luminance adaptation (LA) and blur adaptation (BA) in certain content. Besides, we adjust parameters with subjective experiments to better fit HVS characteristics. Compared with several state-of-the-art JND models, our method tolerates more distortion and owns better perceptual performance under the same injected-noise energy.