Comparison of visual saliency models for compressed video

Visual saliency modeling is an increasingly important research problem. While most saliency models for dynamic scenes operate on raw video, several models have also been developed for compressed video. This paper compares the accuracy of nine such models on a common eye-tracking dataset. The results indicate that a reasonably accurate saliency estimation is possible even using only motion vectors from the compressed bitstream. Successful strategies in compressed-domain saliency modeling are highlighted, and certain challenges are identified for future improvement.