Abstract: With the popularization of virtual reality technology, panoramic image quality assessment faces new chal-lenges. Addressing the issue that existing full-reference methods are mostly simple extensions of tradition-al indicators, while no-reference methods, although relying on deep learning, generally lack interpretabil-ity, this study proposes a full-reference panoramic image quality assessment method based on visual per-ception mechanisms. First, it integrates the advantages of equidistant rectangular projection and cube map-ping projection, and calculates the structural similarity index in the discrete cosine transform domain to more accurately capture key structural features. Then, it introduces the equatorial bias factor to enhance the method's conformity to human visual attention characteristics. Experiments on the CVIQD and MVAQD databases, and comparisons with existing panoramic image quality assessment algorithms, show that the proposed method outperforms the comparative methods in both Pearson linear correlation coefficient (PLCC) and Spearman rank correlation coefficient (SRCC). This method can effectively improve the accu-racy and perceptual consistency of panoramic image quality assessment, providing technical support for optimizing the virtual reality user experience.