  Recently, with the development of deep learning, a number of Just Noticeable
Difference (JND) datasets have been built for JND modeling. However, all the
existing JND datasets only label the JND points based on the level of
compression distortion. Hence, JND models learned from such datasets can only
be used for image/video compression. As known, JND is a major characteristic of
the human visual system (HVS), which reflects the maximum visual distortion
that the HVS can tolerate. Hence, a generalized JND modeling should take more
kinds of distortion types into account. To benefit JND modeling, this work
establishes a generalized JND dataset with a coarse-to-fine JND selection,
which contains 106 source images and 1,642 JND maps, covering 25 distortion
types. To this end, we proposed a coarse JND candidate selection scheme to
select the distorted images from the existing Image Quality Assessment (IQA)
datasets as JND candidates instead of generating JND maps ourselves. Then, a
fine JND selection is carried out on the JND candidates with a crowdsourced
subjective assessment.
