  The spherical domain representation of 360 video/image presents many
challenges related to the storage, processing, transmission and rendering of
omnidirectional videos (ODV). Models of human visual attention can be used so
that only a single viewport is rendered at a time, which is important when
developing systems that allow users to explore ODV with head mounted displays
(HMD). Accordingly, researchers have proposed various saliency models for 360
video/images. This paper proposes ATSal, a novel attention based (head-eye)
saliency model for 360\degree videos. The attention mechanism explicitly
encodes global static visual attention allowing expert models to focus on
learning the saliency on local patches throughout consecutive frames. We
compare the proposed approach to other state-of-the-art saliency models on two
datasets: Salient360! and VR-EyeTracking. Experimental results on over 80 ODV
videos (75K+ frames) show that the proposed method outperforms the existing
state-of-the-art.
