Saliency detection is widely used to extract the regions of interest in images. Many saliency detection models have been proposed for videos in the uncompressed domain. However, videos are always stored in the compressed domain such as MPEG2, H.264, MPEG4 Visual, etc. In this study, we propose a video saliency detection model based on feature contrast in the compressed domain. Four features of luminance, color, texture and motion are extracted from DCT coefficients and motion vectors in the video bitstream. The static saliency map of video frames is calculated based on the luminance, color and texture features, while the motion saliency map for video frames is computed by motion feature. The final saliency map for video frames is obtained through combining the static saliency map and motion saliency map. Experimental results show good performance of the proposed video saliency detection model in the compressed domain.