  The just noticeable difference (JND) is the minimal difference between
stimuli that can be detected by a person. The picture-wise just noticeable
difference (PJND) for a given reference image and a compression algorithm
represents the minimal level of compression that causes noticeable differences
in the reconstruction. These differences can only be observed in some specific
regions within the image, dubbed as JND-critical regions. Identifying these
regions can improve the development of image compression algorithms. Due to the
fact that visual perception varies among individuals, determining the PJND
values and JND-critical regions for a target population of consumers requires
subjective assessment experiments involving a sufficiently large number of
observers. In this paper, we propose a novel framework for conducting such
experiments using crowdsourcing. By applying this framework, we created a novel
PJND dataset, KonJND++, consisting of 300 source images, compressed versions
thereof under JPEG or BPG compression, and an average of 43 ratings of PJND and
129 self-reported locations of JND-critical regions for each source image. Our
experiments demonstrate the effectiveness and reliability of our proposed
framework, which is easy to be adapted for collecting a large-scale dataset.
The source code and dataset are available at
https://github.com/angchen-dev/LocJND.
