A Novel Edge-pattern-based Just Noticeable Difference Model for Screen Content Images
Xueqin Liu, Xuanni Zhan, Miaohui Wang · 2020
Just noticeable difference (JND) reflecting visual redundancy of human visual system (HVS) has been widely adopted in image/video processing. In this work, we introduce a novel JND model in pixel domain for screen content (SC) images. Due to the obvious different impacts on HVS between pictorial region and computer-generated texture, we decompose a SC image into screen content set (SCS) and non-screen content set (non-SCS) for JND modeling. For SCS, edge masking effect is mainly considered by using a parametric model with an adaptive edge representation. For non-SCS, both the edge masking effect and pattern masking effect are taken into account to improve distortion tolerance by injecting more noise to pattern complexity region without perceptual quality degradation. Furthermore, visual saliency is employed to adjust pattern masking effect in the view of HVS. Experimental results show that compared with state-of-the-arts, the proposed JND model can tolerate more distortion, and provide better perceptual quality at the same noise level.