Motion saliency detection in a compressed domain is crucial for various video applications, including retargeting, surveillance, object checking, and segmentation. The goal of this paper is to improve the performances of an existing motion saliency detection model in a compressed domain developed by Fang et al. Specifically, we improve the detection accuracy of motion center-surround features by dynamically fitting the parameters of a Gaussian distribution model. Besides, the parameters for the distribution of distance in horizontal and vertical directions are obtained separately instead of treating them together. In addition, the motion importance features are exploited to strengthen the performance of detection. Experimental results demonstrate that the proposed motion saliency detection method outperforms the existing approaches in both a pixel and compressed domain.