Visual saliency detection based on mutual information in compressed domain

Saliency detection of video sequences has attracted great attention in recent years. In this paper, we propose a saliency detection framework in the compressed domain. Spatial and temporal saliency map are derived by calculating mutual information of feature distribution between center window and surrounding window respectively. Motion vector is used to get the temporal saliency map, and features extracted from the discrete cosine transformation coefficients including luminance, color and texture are used to obtain the spatial saliency map. Then we combine the spatial and temporal saliency maps to obtain a spatiotemporal saliency map. Finally, a convex-hull-based center bias is added to optimize the saliency maps. Experimental results show that the proposed method outperforms the existing state-of-the-art saliency detection methods.