Graph-Based Detection of Seams In 360-Degree Images

Abstract

In this paper, we propose an algorithm to detect a specific kind of distortions, referred to as seams, which commonly occur when a 360-degree image is represented in planar domain by projecting the sphere to a polyhedron, e.g, via the Cube Map (CM) projection, and undergoes lossy compression. The proposed algorithm exploits a graph-based representation to account for the actual sampling density of the 360-degree signal in the native spherical domain. The CM image is considered as a signal lying on a graph defined on the spherical surface. The spectra of the processed and the original signals, computed by applying the Graph Fourier Transform, are compared to detect the seams. To test our method a dataset of compressed CM 360-degree images, annotated by experts, has been created. The performance of the proposed algorithm is compared to those achieved by baseline metrics, as well as to the same approach based on spectral comparison but ignoring the spherical nature of the signal. The experimental results show that the proposed method has the best performance and can successfully detect up to approximately 90% of visible seams on our dataset.

Subjective and Viewport-based Objective Quality Assessment of 360-degree videos

“…(It is expected that such a behavior is even more pronounced when the ERP format is used in the planar domain, which has more re-sampling and geometrical distortion than EAC. ); (iv) the uniform sampling with 40-degree fieldof-view provides the best performance for most of the metrics; (v) none of the metrics performs good enough on seams, which is a highly localized distortion that can be hidden on the global metrics; this result highlights the need for specific artifact metrics, such as [6]; and (vi) finally, it is possible to notice that the performance of the different metrics varies significantly according to the different distortion types and that there is not a single metric that performs the best for each individual distortion.…”