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.

翻译：刚好可察觉差别（JND）是指人类能够察觉到的刺激之间的最小差异。对于给定的参考图像和压缩算法，图像级刚好可察觉差别（PJND）表示导致重建图像出现可察觉差异的最小压缩级别。这些差异仅在图像中的某些特定区域（称为JND关键区域）可被观察到。识别这些区域有助于改进图像压缩算法的开发。由于视觉感知因人而异，确定目标用户群体的PJND值和JND关键区域需要开展涉及足够数量观察者的主观评估实验。本文提出了一种利用众包开展此类实验的新框架。通过应用该框架，我们创建了名为KonJND++的新型PJND数据集，包含300张原始图像及其经JPEG或BPG压缩的版本，每张原始图像平均有43个PJND评级和129个自报告的JND关键区域位置。实验证明了所提出框架的有效性和可靠性，该框架易于适配以收集大规模数据集。源代码和数据集可在https://github.com/angchen-dev/LocJND获取。