動態估測是在視訊壓縮中很重要的一個議題。在過去的動態估測研究皆基於中心基準策略和最小均方誤差趨勢兩種理論基礎，但是這些方法估測效果不好或著會找到區域性的最小值而非全域性最小值；另外一些方法會依據動態向量估測出啟始位置，這些方法速度快但是結果常常不正確。本篇論文的第一部分敘述了之前的方法容易估測效果不好、找到區域性最小值以及結果不正確的缺失。而提出的多向搜尋趨勢 (Multiple Searching Trend: MST) 方法減少了無效率的搜尋和找到區域性最小值的機會，適應性擴張搜尋區域 (Adaptive Dilated Searching Field: ADSF) 則避免了一開始找到錯誤的起始位置的缺點。將以上 MST 和 ADSF 應用在之前提出的動態估測方法將可得到更快而正確的結果。因此本方法被稱之為雞尾酒式搜尋法 (CockTail Search: CTS)。
在本論文的另一部份提出了一個新的估測標準用來尋找在參考畫面的搜尋視窗中跟現在要被估測的最接近而適合的參考區塊。現金要被壓縮的方塊和其參考方塊的畫素差異值之預測誤差 (Prediction Error: PE) 被拿來利用作為比均方誤差效果更好的選擇最適於做視訊壓縮的區塊估測方法。而在本論文中將PE和MSE合併之估測標準能得到比PE更好之效果。此兩種估測標準應用到UCBDS[4]來比較，結果發現UCBDS和本論文提出之標準合併之法比起UCBDS和MSE合併之法壓縮後檔案容量大小縮減了將近40%。

Motion estimation is the key issue in video compressing. Several methods for motion estimation based on the center biased strategy and minimum mean square error trend searching have been proposed, such as TSS, FSS, UCBDS and MIBAS, but these methods yield poor estimates or find local minima. Many other methods predict the starting point for the estimation, these can be fast but are inaccurate. This study addresses the causes of wrong estimates, local minima and incorrect predictions in the prior estimation methods. The Multiple Searching Trend (MST) is proposed to overcome the problems of ineffective searches and local minima, and the Adaptive Dilated Searching Field (ADSF) is described to prevent prediction from wrong location. Applying MST and ADSF to the listed estimating methods, such as UCBDS, a fast and accurate can be reached. For this this reason, the method is called CockTail Searching (CTS).
In another proposed method, we try to define the new criteria used to determine a referent macro block within the search window in a referent frame, which matches the estimated current macro block in the current frame, in motion estimation process used in MPEG standard. The Prediction Error(PE) in the Pixel Difference(PD) between the referent macro block and the current macro block is defined to be a new criterion which can get better performance in compressed data length than the Mean Square Error(MSE) used by most of motion estimation methods. The other criterion combined PE and MSE is proposed to get better performance than the PE. Two new criteria is applied to a famous motion estimation method, UCBDS, to show the performance of the new criteria. The evaluation results show that using new criteria in UCBDS can get more 40% reduction in compressed data size than the UCBDS with MSE.

中文摘要……………………………………………………………………………..i
Abstract……………………………………………………………………………...iii
List of Figures………………………………………………………………………..iv
List of Tables……………………………………………………………………….....v
Chapter 1 Introduction………………………………………………………………1
Chapter 2 Motion Estimation for Video Compressing…………….……...……..7
Chapter 3 Motivations….….………...……………………………………………21
3.1 Observation in Existing Motion Estimation Methods………………………21
3.2 The Confusions with Mean Square Error………….……………………..32
Chapter 4 The Proposed Methods…………………………………………………38
4.1 Cocktail Search Algorithm (CTS).…………………………………………38
4.2 Criteria for Motion Estimation to Increase Compression Rate……………..46
4.3 UCBDS-mPEN: a Novel Motion Estimation Method to Obtain Higher Compression Rate………………………………………………………………50
Chapter 5 Simulations and Discussions……………………………………...55
5.1 Evaluating Results of CockTail Search Scheme……………………………55
5.2 Evaluating Results of UCBDS-mPEN……………………………………...68
Chapter 6 Conclusions………………..…………………………………………...72
Reference…………………………………………………………………………….73

[1] Ahmadianpour, F. and Ahmad, M.O.,‘A fast motion estimation method using an enhanced motion vector and DC matching methodology’, 10th International Conference on Telecommunications, vol.2, pp:1465-1469, Feb. 2003.
[2] Al-Regib, G.; Altunbasak, Y. and Mersereau, R.M.,‘Hierarchical motion estimation with content-based meshes’, IEEE Trans. Circuits Syst. Video Technol., vol.13, pp. 1000-1005, Oct. 2003.
[3] Chan, Y.-L. and Siu, W.-C., ‘An adaptive partial distortion search for block motion estimation’, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, vol: 3 , Pages:III - 153-6, Apr. 2003.
[4] Chen, J.; Cai, C. and Ding, R., ‘Oversampled wavelet motion compensation and its hierarchical block-matching algorithm’, 2003 IEEE International Conference on Neural Networks and Signal Processing, vol.2, pp:1270 – 1273, Dec. 2003.
[5] Cheung, C.-H. and Po, L.-M., ‘A Novel Cross-Diamond Search Algorithm for Fast Block Motion Estimation’, IEEE Trans. Circuits Syst. Video Technol., vol. 12, pp. 1168-1177, Dec. 2002.
[6] Cheung, C.-H. and Po, L.-M., ‘Adjustment Partial Distortion Search Algorithm for Fast Block Motion Estimation’, IEEE Trans. Circuits Syst. Video Technol., vol. 13, pp. 100-110, Jan. 2003.
[7] Christopoulos, V.; Cornelis, J., ‘A center-biased adaptive search algorithm for block motion estimation,’ IEEE Trans. Circuits Syst. Video Technol., vol. 10, pp. 423-426, Apr. 2000.
[8] Duanmu, C.J.; Ahmad, M.O. and Swamy, M.N.S.,’A new lower bound for fast block motion estimation algorithms’, 2003 IEEE Canadian Conference on Electrical and Computer Engineering, vol. 3, pp:1975 – 1980,, May 2003.
[9] Duanmu, C.J.; Ahmad, M.O. and Swamy, M.N.S.;, ‘A fast three-step search algorithm by the utilization of multilevel vector partial sums’, 2003 IEEE Canadian Conference on Electrical and Computer Engineering, vol.3, pp:1981 - 1984, May 2003.
[10] Gao, X.-Q.; Duanmu, C.-J. and Zou, C.-R., ‘A multilevel successive elimination algorithm for block matching motion estimation,’ Image Processing, vol. 9, pp. 501-504, Mar. 2000.
[11] Han, K. and Chun, B., ‘Adaptive hexagon search pattern for block motion estimation’, 2003 IEEE International Conference on Systems, Man and Cybernetics, vol.2, pp:1406 - 1409, Oct. 2003.
[12] Hong, W.-G. and Oh, T.-M., ’Sorting-based partial distortion search algorithm for motion estimation’ , Electronics Letters, vol.40, pp. 113-115, Jan. 2004.
[13] Hong, W.-G. and Oh, T.-M.,’Enhanced partial distortion search algorithm for block motion estimation’, Electronics Letters, vol. 39, pp. 1112 – 1113, Jul. 2003.
[14] Huang, Y.-W.; Ma, S.-Y.; Shen, C.-F. and Chen, L.-G..,‘Predictive line search: an efficient motion estimation algorithm for MPEG-4 encoding systems on multimedia processors’, IEEE Trans. Circuits Syst. Video Technol., vol.13, pp. 111-117, Jan. 2003.
[15] Ismaeil, I.; Docef,A.; Kossentini, F. and Ward, R., ‘Efficient motion estimation using spatial and temporal motion vector prediction,’ in Proc. ICIP 99, pp. 70-74, Oct. 1999.
[16] Jia, H. and Zhang, L. ,‘Directional diamond search pattern for fast block motion estimation’, Electronics Letters, vol.39, pp.1581 – 1583, Oct. 2003.
[17] Jou, J.-M.; Chen, P.-Y. and Sun, J.-M., ‘The gray prediction search algorithm for block motion estimation,’ IEEE Trans. Circuits Syst. Video Technol., vol. 9, pp. 843-848, Sept. 1999.
[18] Kim, S.-J.; Ahn, J.-H. and Yim, C., ‘Adaptive motion estimation algorithm for MPEG-4 video coding’, IEEE Seventh International Symposium on Signal Processing and Its Applications, vol. 2, pp:141 – 144, Jul. 2003.
[19] Koga, T.; Iinurna, K.; Hirano, A.; Iijima, Y. and Ishiguro, T., ‘Motion-compensated interframe coding for video conferencing,’ in Proc. NTC 81, pp. C9.6.1-9.6.5, New Orleans, LA, Nov./Dec. 1981.
[20] Li, Reoxiang; Zeng, Bing and Liou, M.L., ‘A new three-step search algorithm for block motion estimation,’ IEEE Trans. Circuits Syst. Video Technol., Vol. 4, pp. 438-442, Aug. 1994.
[21] Li, W. and Salari, E., ‘Successive elimination algorithm for motion estimation,’ IEEE Trans. Image Processing, vol. 4, pp. 105-107, Jan. 1995.
[22] Li, Y. and Oraintara, S.,’ A novel adaptive multi-mode search algorithm for fast block-matching motion estimation’, Proceedings of the 2004 International Symposium on Circuits and Systems 2004, vol.3, pp. III-977-III-980, May 2004.
[23] Lim, Y.-C.; Min, K.-Y. and Chong, J.-W., ‘A pentagonal fast block matching algorithm for motion estimation using adaptive search range’, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, pp:III - 669-672, vol. 3, Apr. 2003.
[24] Luo, L.; Zou, C.; X. Gao, and He, Z., ‘A new prediction search algorithm for block motion estimation in video coding,’ IEEE Trans. Consumer Electron., vol. 43, pp. 56-61, Feb. 1997.
[25] Mietens, S. and Hentschel, C., ’Computational-complexity scalable motion estimation for mobile MPEG encoding’, IEEE Trans. Circuits Syst. Video Technol., vol.50, pp. 281-291, Feb. 2004.
[26] Moschetti, F.; Kunt, M. and Debes, E.; ‘A statistical adaptive block-matching motion estimation’, IEEE Trans. Circuits Syst. Video Technol., vol.13, pp. 147-431, May 2003.
[27] Nie, Y. and Ma, K.-K., ‘Adaptive Rood Pattern Search for Fast Block–Matching Motion Estimation’, IEEE Trans. Image Processing, vol 11, pp. 1442-1449, Dec. 2002.
[28] Okuda, H.; Hashimoto, M.; Sumi, K. and Sasaki, K., ‘Optimum selection algorithm of motion estimation blocks for fast and robust digital image stabilization’, 2003 IEEE International Conference on Consumer Electronics, pp:396 - 397, Jun. 2003.
[29] Po, L.-M. and Ma, W.-C. ‘A novel four-step search algorithm for fast block motion estimation,’ IEEE Trans. Circuits Syst. Video Tcehnol., vol. 6, pp. 313-317, June 1996.
[30] Ramkishor, K.; Raghu, T.S. and Suman, K.; Gupta, P.S.S.B.K.;Spatial correlation based fast field motion vector estimation algorithm for interlaced video encoding’, 2003 IEEE International Conference on Multimedia and Expo., vol.2, pp:II - 797-800, Jul. 2003.
[31] Su, R.; Liu, G and Zhou, G.,’A novel fast sampling predictive block match algorithm’, 2003 IEEE International Conference on Neural Networks and Signal Processing, vol.2, pp:1197 – 1200, Dec. 2003.
[32] Tham, J. Y.; Ranganath, S.; Ranganath, M. and Kassim, A.-A., ‘A novel unrestricted center-biased diamond search algorithm for block motion estimation,’ IEEE Trans. Circuits Syst. Video Technol., vol. 8, pp. 369-377, Aug. 1998.
[33] Tourapis, A.-M.; Au, O.-C. and Liou, M.-L., ‘Highly Efficient Predictive Zonal Algorithms for Fast Block-Matching Motion Estimation’, IEEE Trans. Circuits Syst. Video Technol., vol. 12, pp. 934-947, Oct. 2002.
[34] Tu, Y.-K.; Yang, J.-F.; Shen, Y.-N. and Sun, M.-T.,’Fast variable-size block motion estimation using merging procedure with an adaptive threshold’, 2003 IEEE International Conference on Multimedia and Expo, vol. 2, pp. Pages:II - 789-792, Jul. 2003.
[35] Yang, T.; Zhu, C. and Peng, Q., ‘A controllable predictive cross-diamond fast search algorithm for block matching motion estimation’, proceedings of the Fourth International Conference on Parallel and Distributed Computing, Applications and Technologies, pp:821 - 824, Aug. 2003.
[36] Yoon, H. and Lee, G.,’Motion estimation based on spatio-temporal correlations’, 2003 IEEE International Conference on Image Processing, vol.2, pp.359-362, Sep. 2003.
[37] Zhu, C.; Lin, X. and Chau, L.-P., ‘Hexagon-Based Search Pattern for Fast Block Motion Estimation’, IEEE Trans. Circuits Syst. Video Technol., vol. 12, pp 349-355, May 2002.