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Optimal Motion Vector Search Algorithm - Final Presentation

Optimal Motion Vector Search Algorithm - Final Presentation. 6th Team 20032077 Jung, Yu-Chul 20032026 Kim, Hyun-Seok 20032072 Jang, Sun-Yean. ☆ Overview ☆. Our goal and Requirements What is implemented.. Implementation Demo Analysis I, II Conclusion References.

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Optimal Motion Vector Search Algorithm - Final Presentation

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  1. Optimal Motion Vector Search Algorithm - Final Presentation 6th Team 20032077 Jung, Yu-Chul 20032026 Kim, Hyun-Seok 20032072 Jang, Sun-Yean

  2. ☆ Overview ☆ • Our goal and Requirements • What is implemented.. • Implementation Demo • Analysis I, II • Conclusion • References

  3. ☆ Our goal and requirements☆ • 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) •  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

  4. ☆ What is implemented.. ☆ Points 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 3. Use Nearest Neighbor hood Algorithm :To reduce the possibility of getting trapped in local minimum, To improve prediction accuracy

  5. ☆ Implementation Demo☆

  6. ☆ Analysis I☆ • Performance of the mentioned approaches Execution time depends on implementation and CPU resource. Thus, it can’t be considered deterministic point.

  7. ☆ 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

  8. ☆ 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

  9. ☆ References ☆ • 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) • 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 • 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 1999 • Computation constrained fast-search motion estimation algorithm for TMN 7. In Q15-A-45, ITU-T Q15/SG16, Portland, Oregon, June 1997 • http://www.dcs.warwick.ac.uk/research/mcg/bmmc/index.html

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