Cube-SSIM: A Metric for Evaluating 360-degree Images as Cube Maps

360-degree image data is a crucial aspect in graphics applications where they are typically used for lighting purposes. Fields like mixed reality generally rely on lighting estimation techniques to estimate the 360-degree environment. To evaluate such approaches, accurate image assessment in this domain is important. However, traditional image evaluation metrics like SSIM, PSNR, and IMED are problematic when analyzing 360-degree image data as they would require two-dimensional representations like equirectangular panoramas or unfolded cubes that introduce image distortions. In this paper, we address this problem by presenting Cube-SSIM, a variant of SSIM designed specifically for cube maps. For this, we modify SSIM to take the solid angles of cube map pixels into account which gives more consistent results than using SSIM for the individual cube faces. The computations can run efficiently on graphics hardware due to their native support for cube maps and no further image conversions are required. We show that our approach allows for more accurate results than other comparison metrics that largely depend on 2D images. While SSIM is especially important due to its wide usage, the modification can also be applied to other image metrics for which we include IMED as an example.