Subjective quality assessment remains the most reliable way to evaluate image quality while being tedious and money consuming. Therefore, objective quality evaluation ensures a trade-off by providing a computational approach for predicting image quality. Even though a large literature exists for 2D image and video quality evaluation, 360-degree images quality is still under-explored. One can question the efficiency of 2D quality metrics on such a new type of content. To this end, we propose to study the possible improvement of well-known 2D quality metrics using important features related to 360-degree content, i.e. equator bias and visual saliency. The performance evaluation is conducted on two databases containing various distortion types. The obtained results show a slight improvement of the performance highlighting some problems inherently related to both the database content and the subjective evaluation approach used to obtain the observers’ quality scores.