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Saliency prediction can be of great benefit for 360-degree image/videoapplications, including compression, streaming , rendering and viewpointguidance. It is therefore quite natural to adapt the 2D saliency predictionmethods for 360-degree images. To achieve this, it is necessary to project the360-degree image to 2D plane. However, the existing projection techniquesintroduce different distortions, which provides poor results and makesinefficient the direct application of 2D saliency prediction models to360-degree content. Consequently, in this paper, we propose a new framework foreffectively applying any 2D saliency prediction method to 360-degree images.The proposed framework particularly includes a novel convolutional neuralnetwork based fusion approach that provides more accurate saliency predictionwhile avoiding the introduction of distortions. The proposed framework has beenevaluated with five 2D saliency prediction methods, and the experimentalresults showed the superiority of our approach compared to the use of weightedsum or pixel-wise maximum fusion methods.

Extending 2D Saliency Models for Head Movement Prediction in 360-degree Images using CNN-based Fusion