Iterative least squares and compression based estimations for a four-parameter linear global motion model and global motion compensation

In this paper, a four-parameter model for global motion in image sequences is proposed. The model is generalized and can accommodate global object motions besides the motions due to the camera movements. Only the PAN and the ZOOM global motions are considered because of their relatively more frequent occurrences in real video sequences. Besides the traditional leastsquares estimation scheme, two more estimation schemes based on the minimization of the motion field bit rate and the global prediction error energy are proposed. Among the three estimation schemes, the iterative least-squares estimation is observed to be the best because of the least computational complexity, accuracy of the estimated parameters, and similar performance as with the other schemes. Four global motion compensation schemes including the existing pixel-based forward compensation are proposed. It is observed that backward compensation schemes perform similarly to the corresponding forward schemes except for having one frame delay degradation. The pixel-based forward compensation is observed to have the best performance. A new motion vector coding scheme is proposed which has similar performance as the two-dimensional entropy coding but needs much less computation. Using the proposed coding scheme with the pixel-based forward compensation, we obtain 61.85% savings in motion field bit rate over the conventional motion compensation for the Tennis sequence.