Saliency-Guided Just Noticeable Distortion Estimation Using the Normalized Laplacian Pyramid

Abstract

The human visual system (HVS), like any other physical system, has limitations. For instance, it is known that the HVS can only sense the content changes that are larger than the so-called just noticeable distortion (JND) threshold. Also, to reduce the computational load on the brain, the visual attention mechanism is deployed such that regions with higher visual saliency are processed with higher priority than other less-salient regions. It is also known that visual saliency has a modulatory effect on JND thresholds. In this letter, we present a novel pixel-wise JND estimation method that considers the interplay between visual saliency and JND thresholds. In the proposed method, the largest JND thresholds of a given image are found such that the perceptual distance between the image and its JND noise-contaminated version is minimized in a perceptual space defined by the coefficients of the image in a normalized Laplacian pyramid. Experimental results indicate that the proposed method outperforms four of the latest JND models for static images.