  Effective compression of 360$^\circ$ images, also referred to as
omnidirectional images (ODIs), is of high interest for various virtual reality
(VR) and related applications. 2D image compression methods ignore the
equator-biased nature of ODIs and fail to address oversampling near the poles,
leading to inefficient compression when applied to ODI. We present a new
learned saliency-aware 360$^\circ$ image compression architecture that
prioritizes bit allocation to more significant regions, considering the unique
properties of ODIs. By assigning fewer bits to less important regions,
significant data size reduction can be achieved while maintaining high visual
quality in the significant regions. To the best of our knowledge, this is the
first study that proposes an end-to-end variable-rate model to compress
360$^\circ$ images leveraging saliency information. The results show
significant bit-rate savings over the state-of-the-art learned and traditional
ODI compression methods at similar perceptual visual quality.
