On the Improvement of 2D Quality Assessment Metrics for Omnidirectional Images

Abstract

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.