1. An Efficient Search Strategy for Block Motion Estimation Using Image Features Digital Video Processing 1 Term Project Feng Li Michael Su Xiaofeng Fan

2. Fast Search in Block Motion Estimation Block motion estimation using the full search is very computational demanding.  225 search points in a 15x15 search window with displacement vectors from -7 to 7 in X, Y directions. Fast search algorithms to reduce the amount of computations by limiting the number of locations to be searched. Fast search algorithms can be trapped on the local minimum in the search process.

3. Nonuni-modal error surface tested by a checking block

4. MAD error surface for two different blocks

5. Fast search improvement Place the checking point as close as possible to the global minimum. Multiple starting search points. Eliminate the unnecessary starting points using image features. Points, Lines and Edges can be used as the image features.

6. Regular Starting point pattern: Starting points distribute evenly across the search window

7. Image Features Edge-Assisted Search (EAS) Edges are used as the image features. The number of search points are dynamic on each block. Less search points are used for smooth region. More search points are needed in blocks containing many edges and motions.

8. Image Features Edge-Assisted Search (EAS) Image preprocessing  Smoothing of the frame.  Edge Detection using 3x3 Sobel gradient convolution masks.

9. Adjustment of the Regular Starting Point Pattern Convert into the binary image by threshold. Edge matching score (EMS) to eliminate the unnecessary starting points.

10. Flowchart of the EAS

11. Example 1

12. Image Block (15X15) and edge difference distribution

13. Image Block (25X25) and edge difference distribution

14. Block Intensity difference

15. Test result 1: Displacement Vectors for Example 1

16. Example 2

17. Test result 2: Displacement Vectors for Example 2

18. Performance Evaluation

19. Thank you Questions?