  Along with the development of virtual reality (VR), omnidirectional images
play an important role in producing multimedia content with immersive
experience. However, despite various existing approaches for omnidirectional
image stitching, how to quantitatively assess the quality of stitched images is
still insufficiently explored. To address this problem, we establish a novel
omnidirectional image dataset containing stitched images as well as
dual-fisheye images captured from standard quarters of 0$^\circ$, 90$^\circ$,
180$^\circ$ and 270$^\circ$. In this manner, when evaluating the quality of an
image stitched from a pair of fisheye images (e.g., 0$^\circ$ and 180$^\circ$),
the other pair of fisheye images (e.g., 90$^\circ$ and 270$^\circ$) can be used
as the cross-reference to provide ground-truth observations of the stitching
regions. Based on this dataset, we further benchmark six widely used stitching
models with seven evaluation metrics for IQA. To the best of our knowledge, it
is the first dataset that focuses on assessing the stitching quality of
omnidirectional images.
