Enhanced Just Noticeable Difference (JND) estimation with image decomposition

Contrast masking (CM) on edge and textured regions have to be distinguished since distortions on edge regions are easier to be noticed than that on textured regions. Therefore, how to efficiently estimate the CM on edge and textured regions of an image is a key issue for accurate JND (Just Noticeable Difference) estimation. An enhanced image domain JND estimator is devised in this paper with new model for CM. We use the total variation method to obtain a structural image (which contains edge information) and a textural image (which contains texture information) from the input image, and then evaluate the CM for the two images separately rather than the whole image, and hence edge and texture are better distinguished and the under-estimation of JND on textured regions can be effectively avoided. Experimental results of subjective viewing confirm that the proposed model is capable of determining more accurate visibility thresholds.