  Just noticeable difference (JND) of natural images refers to the maximum
pixel intensity change magnitude that typical human visual system (HVS) cannot
perceive. Existing efforts on JND estimation mainly dedicate to modeling the
diverse masking effects in either/both spatial or/and frequency domains, and
then fusing them into an overall JND estimate. In this work, we turn to a
dramatically different way to address this problem with a top-down design
philosophy. Instead of explicitly formulating and fusing different masking
effects in a bottom-up way, the proposed JND estimation model dedicates to
first predicting a critical perceptual lossless (CPL) counterpart of the
original image and then calculating the difference map between the original
image and the predicted CPL image as the JND map. We conduct subjective
experiments to determine the critical points of 500 images and find that the
distribution of cumulative normalized KLT coefficient energy values over all
500 images at these critical points can be well characterized by a Weibull
distribution. Given a testing image, its corresponding critical point is
determined by a simple weighted average scheme where the weights are determined
by a fitted Weibull distribution function. The performance of the proposed JND
model is evaluated explicitly with direct JND prediction and implicitly with
two applications including JND-guided noise injection and JND-guided image
compression. Experimental results have demonstrated that our proposed JND model
can achieve better performance than several latest JND models. In addition, we
also compare the proposed JND model with existing visual difference predicator
(VDP) metrics in terms of the capability in distortion detection and
discrimination. The results indicate that our JND model also has a good
performance in this task.
