Pattern Masking Estimation in Image With Structural Uncertainty

Abstract

A model of visual masking, which reveals the visibility of stimuli in the human visual system (HVS), is useful in perceptual based image/video processing. The existing visual masking function mainly considers luminance contrast, which always overestimates the visibility threshold of the edge region and underestimates that of the texture region. Recent research on visual perception indicates that the HVS is sensitive to orderly regions that possess regular structures and insensitive to disorderly regions that possess uncertain structures. Therefore, structural uncertainty is another determining factor on visual masking. In this paper, we introduce a novel pattern masking function based on both luminance contrast and structural uncertainty. Through mimicking the internal generative mechanism of the HVS, a prediction model is firstly employed to separate out the unpredictable uncertainty from an input image. In addition, an improved local binary pattern is introduced to compute the structural uncertainty. Finally, combining luminance contrast with structural uncertainty, the pattern masking function is deduced. Experimental result demonstrates that the proposed pattern masking function outperforms the existing visual masking function. Furthermore, we extend the pattern masking function to just noticeable difference (JND) estimation and introduce a novel pixel domain JND model. Subjective viewing test confirms that the proposed JND model is more consistent with the HVS than the existing JND models.