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Abstract
The just noticeable difference (JND) models in pixel domain are generally composed of luminance adaptation (LA) and contrast masking (CM), which takes edge masking (EM) and texture masking (TM) into consideration. However, in existing pixel-wise JND models, CM is not evaluated appropriately since they overestimate the masking effect of regular oriented texture regions and neglect the visual attention characteristic of human eyes for the real image. In this work, a novel JND model in pixel domain is proposed, where orderly texture masking (OTM) for regular texture areas (also called orderly texture regions) and disorderly texture masking (DTM) for complex texture areas (also called disorderly texture regions) are presented based on the orientation complexity. Meanwhile, the visual saliency is set as the weighting factor and is incorporated into CM evaluation to enhance JND thresholds. Experimental results indicate that compared with existing relevant JND profiles, the proposed JND model tolerates more distortion in the same perceptual quality, and brings better visual perception in the same level of the injected JND-noise energy.
Highlights
Images/Videos are commonly explored in various multimedia services and become an indispensable part in people’s daily life
An important perceptual characteristic of human visual system (HVS) is that it presents limited visual sensitivity to the images/videos, only the pixel changes above a certain visibility threshold can be observed by human eyes [1]
The just noticeable difference (JND) models are widely applied on variable kinds of perceptual-oriented image/video related tasks, such as perceptual compression [7]–[9], perceptual quality assessment [10], [11], watermarking [12], display [13], to name a few
Summary
Images/Videos are commonly explored in various multimedia services and become an indispensable part in people’s daily life. An important perceptual characteristic of human visual system (HVS) is that it presents limited visual sensitivity to the images/videos, only the pixel changes above a certain visibility threshold can be observed by human eyes [1]. The objective of this work is to design an effective pixel-wise JND model to accurately describe characteristics of HVS on images. Based on Chou and Li [19], Yang et al [20] exploited a nonlinear additivity model to reduce the overlapping effects between LA and CM Since these two methods overestimated the masking effects in the edge regions, Liu et al [15] decomposed one input image into two images, one is named structural image and the other is the textural image, followed by performing edge masking (EM) estimation and texture masking (TM) estimation, respectively.
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