A new block-based motion estimation algorithm for image sequence coding is proposed. The motion vectors between neighboring blocks are highly correlated, and the correlation exists in two dimensions in general. We model the 2-D motion correlation with two separate 1-D models in the horizontal and vertical directions, respectively. The 1-D Kalman filtering is then used to obtain the estimates in the two directions. By linearly combining the two estimates, we develop a motion estimation algorithm that exploits the motion correlation in two dimensions in a simple manner. The algorithm overcomes the difficulty of the general 2-D Kalman filter, which is very complicated and computationally intensive. The results indicate that the proposed algorithm achieves a significant reduction in computation with better performance as compared with the conventional full search algorithm.