Concrete Mathematics Digital Media Lab KIM, HYUNSEOK / JANG, SUNYEAN / JUNG, YUCHUL Optimal Motion Vector Search Algorithm - Final Presentation 6th Team Jung, Yu-Chul Kim, Hyun-Seok Jang, Sun-Yean

Concrete Mathematics Digital Media Lab KIM, HYUNSEOK / JANG, SUNYEAN / JUNG, YUCHUL ☆ Overview ☆ - Our goal and Requirements - What is implemented.. - Implementation Demo - Analysis I, II - Conclusion - References

Concrete Mathematics Digital Media Lab KIM, HYUNSEOK / JANG, SUNYEAN / JUNG, YUCHUL Suggest an optimal method to find the best matching block from an earlier frame to construct an area of the current frame (in here, 8X8 blocks of pixels) in best-search time (  own contribution) ☆ Our goal and requirements ☆  Implement the previous motion vector search algorithms  Contribution to representative value for motion vector search  Combination with the nearest neighborhood algorithm, hash table based on the representative value.  Showing the searched result visually.  Performance Analysis based on the comparison with the previous approaches and our implementation

Concrete Mathematics Digital Media Lab KIM, HYUNSEOK / JANG, SUNYEAN / JUNG, YUCHUL ☆ What is implemented.. ☆ 3. Use Nearest Neighbor hood Algorithm :To reduce the possibility of getting trapped in local minimum, To improve prediction accuracy 3. Use Nearest Neighbor hood Algorithm :To reduce the possibility of getting trapped in local minimum, To improve prediction accuracy 1. Employ a representative value based on location and 8x8 bits information 1. Employ a representative value based on location and 8x8 bits information 2. Use memory hash table : To reduce computational time in searching candidate blocks To embrace generality 2. Use memory hash table : To reduce computational time in searching candidate blocks To embrace generality Points

Concrete Mathematics Digital Media Lab KIM, HYUNSEOK / JANG, SUNYEAN / JUNG, YUCHUL ☆ Implementation Demo ☆

Concrete Mathematics Digital Media Lab KIM, HYUNSEOK / JANG, SUNYEAN / JUNG, YUCHUL ☆ Analysis I ☆ -Performance of the mentioned approaches 1 Full search (2w+1)^2 2 Conjugate Search 3+2w 3 Logarithm Search 2+7log(w) 4 Our Approach Less than (3) Execution time depends on implementation and CPU resource. Thus, it can’t be considered deterministic point.

Concrete Mathematics Digital Media Lab KIM, HYUNSEOK / JANG, SUNYEAN / JUNG, YUCHUL ☆ Analysis II ☆ -Full search is simple and correct, but computational burden. -Other approaches are apt to get trapped in local minima, resulting in a significant loss in estimation accuracy, and compression performance in video coding, as compared to the Full search ☆ However, if we use our implementation, we can - avoid local minima using memory hash table -reduce searching time using nearest neighborhood in finding a best matching block

Concrete Mathematics Digital Media Lab KIM, HYUNSEOK / JANG, SUNYEAN / JUNG, YUCHUL ☆ Conclusion ☆ -Pros -Enable finding the best matching block based on global minima -Linear time search algorithm -Cons -Prerequisite hash table formation time is needed -If applied complex application, memory shortage is estimated -Further works -Research about more advanced representative value -Motion vector search based on object

Concrete Mathematics Digital Media Lab KIM, HYUNSEOK / JANG, SUNYEAN / JUNG, YUCHUL 1.Correlation Based Search Algorithms for Motion Estimation Mohamed Alkanhal, Deepak Turgaga and Tsuhan Chen – E/CE of CMU, USA (Picture Coding Symposium Portland, OR, April 21~23, 1999) 2.An Efficient Computation-Constrained Block-Based Motion Estimation Algorithm for Low Bit Rate Video Coding Michael Gallant and Faouzi Kossentini – E/CE of UBC, Canada 3.Motion Vector Refinement for High-Performance Transcoding Jeongnam Youn, Ming-Ting Sun, Fellow, IEEE, and Chia-Wen Lin IEEE Transaction on Multimedia, Vol. 1, No. 1, March Computation constrained fast-search motion estimation algorithm for TMN 7. In Q15- A-45, ITU-T Q15/SG16, Portland, Oregon, June ☆ References ☆