Enhanced Just Noticeable Difference Model for Images with Pattern Complexity.

The just noticeable difference (JND) in an image, which reveals the visibility limitation of the human visual system (HVS), is widely used for visual redundancy estimation in signal processing. To determine the JND threshold with the current schemes, the spatial masking effect is estimated as the contrast masking, and this cannot accurately account for the complicated interaction among visual contents. Research on cognitive science indicates that the HVS is highly adapted to extract the repeated patterns for visual content representation. Inspired by this, we formulate the pattern complexity as another factor to determine the total masking effect: the interaction is relatively straightforward with limited masking effect in a regular pattern, and is complicated with strong masking effect in an irregular pattern. From the orientation selectivity mechanism in the primary visual cortex, the response of each local receptive field can be considered as a pattern; therefore, in this work, the orientation that each pixel presents is regarded as the fundamental element of a pattern, and the pattern complexity is calculated as the diversity of the orientation in a local region. Finally, taking both pattern complexity and luminance contrast into account, a novel spatial masking estimation function is deduced, and an improved JND estimation model is built. Experimental results on comparing with the latest JND models demonstrate the effectiveness of the proposed model, which performs highly consistent with the human perception.