Spatial just noticeable difference (JND) refers to the smallest amplitude of variation that can be reliably detected by the Human Visual System (HVS). Several studies tried to define models based on thresholds obtained under controlled experiments for conventional 2D or 3D imaging. While the concept of JND is almost mastered for the latter types of content, it is legitimate to question the validity of the results for Extended Reality (XR) where the observation conditions are significantly different. In this paper, we investigate the performance of well-known 2D-JND models on 360-degree images. These models are integrated into basic quality assessment metrics to study their ability to improve the quality prediction process with regards to the human judgment. Here, the metrics serve as tools to assess the effectiveness of the JND models. In addition, to mimic the 360-deg conditions, the equator bias is used to balance the JND thresholds. Globally, the obtained results suggest that 2D-JND models are not well adapted to the extended range conditions and require in-depth improvement or re-definition to be applicable. The slight improvement obtained using the equator bias demonstrates the potential of taking into account XR characteristics and opens the floor for further investigations.