Feature Selection and Robustness Analysis in Global Motion Estimation
He Yu · Chinese Journal of Computers · 2001
This paper studies good feature selection and robustness analysis of global motion estimation. At present the global motion estimation is applied widely, such as in sprite coding, virtual reality and panoramic image generation. But it is difficult to reach the balance of the speed and precision, so doing research on selection of good features and robust calculation to global motion estimation is necessary. The difficulty of global motion estimation is that it is apt to be influenced when local motion of foreground exists. Good feature used in global motion estimation can accelerate the calculation and robustness analysis can improve the accuracy of calculation. This paper proposes the criterion to select good features and a robust calculation for global motion estimation in global motion compensation coding. Motion feature is utilized for global motion estimation according to spatial gradient in the feature selection. There are two methods to exclude the noise in robustness calculation. One is based on residual histogram and the other is based on residual block. Adaptive weight function is added in the objective function in robustness analysis. The influence of noise can be restrained when motion features are used in calculation, and also the convergence of iterative calculation can be accelerated. Comparative experiments are performed to validate those proposals. Some sequences with global motion are utilized in the comparison experimentation. From the experimental results, conclusion can be made that the calculation of global motion estimation can be accelerated and the precision of results can be guaranteed using the good features and robust calculation. The effectiveness can be observed from the comparisons.