A graph convolution neural network-based significance prediction method and system for a 360 degree image. The method comprises: first, using a geodesic icosahedral composition method to construct an image in an equidistant rectangular projection format into a spherical graph signal; then, inputting the spherical graph signal into a proposed graph convolution neural network, performing feature extraction, and generating a spherical significance graph signal; and using a proposed interpolation algorithm based on a spherical crown to construct the spherical significance graph signal into a significance graph in the equidistant rectangular projection format. Furthermore, a KL divergence loss function having sparse consistency is proposed.
SIGNIFICANCE PREDICTION METHOD AND SYSTEM FOR 360 DEGREE IMAGE