GME (Global Motion Estimation) is an important tool widely used in computer vision, video processing, and other fields. In this paper, we propose an efficient, robust, and fast method for the estimation of global motion from compressed image sequences. With regard to global motion models, we adopt six-parameter affine model because of its reasonable tradeoff between complexity and accuracy. In order to improve accuracy and computational efficiency of global motion estimation, we present a new algorithm for segmentation between background and foreground. Then, motion vectors samples associated with background macroblocks are selected to estimate motion model parameters. Lastly, according to the statistics of estimated error, some sample pairs may be rejected as outliers to compensate further for the fact that some of the samples obtained from the P-frame motion vectors are highly erroneous and the parameters may be refined by estimating from the remaining data. The extensive experiments show that the proposed method is efficient and robust in terms of both computational complexity and accuracy.