本論文首先提出一個綜合3DRS與 block-based 技術以及量測運動向量信賴度 (Confidence level) 的 True motion 演算法。此一方法可適用於各種不同型態的視訊 (如靜態、動態、縮放、推移等)。一般而言、將三度空間的真實運動以二維空間區塊平移來表示必然帶來各式失真，因此針對各個block估算的運動向量需做正確性評估，分析其可靠度等級 (Confidence level)。由於視訊影像空間及時間上的連續性，將可靠度較高的運動向量保留與傳遞使用。應用方面將真實運動向量套入動態補償概念，設計與實作一個Noise reduction演算法。實驗結果顯示，此方法可以達到降低噪訊的目的，同時保留影片原有的細節效果。

In this thesis, a novel true motion estimation method and its application on noise reduction for video sequences are proposed. This estimation method is based on the mixture of 3DRS and block-based searching, along with a confidence model of true motion. It can deal with various kinds of true motion (stationary, moving, camera zooming, panning, etc.) in versatile video sequences. Because of the inherent nature of distortion in projecting 3D motion to a series of 2D motion frames, we deliberately evaluate the reliability of each estimated vector so as to ascertain its fidelity. Once the reliability (or called the true motion confidence) secured, the motion vector having the better confidence level will be retained and propagated to the neighboring blocks in the recursive search procedure. Moreover, in this thesis, a compensation-based noise reduction algorithm for video sequences is proposed, which applies the above technique and other capabilities as well to demonstrate the fidelity performance. Experimental results show that this noise reduction algorithm cleanup the noises to an acceptable level while preserving the details in texture.

[1] A. Verdi, T. Poggio, “Motion Field and Optical Flow: Qualitative Properties,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 11, no. 5, pp. 490-498, May 1989.
[2] Y. Wang, J. Ostermann, Y. Q. Zhang, “Video Processing and Communications,” Prentice-Hall, pp. 609, 2002.
[3] A.-M. Huang and T. Nguyen, ”Motion Vector Processing Based On Trajectory Curve Analysis for Motion Compensated Frame Interpolation,” IEEE Int. Conf. Image Processing, pp. 377 – 380, 2009.
[4] A.-M. Huang and T. Nguyen, “Correlation-based motion vector processing method for motion compensated frame interpolation,” IEEE Int. Conf. Image Processing, pp. 1244-1247, 2008.
[5] G. de Haan, P. W. A. C. Biezen, H. Huijgen, and O. A. Ojo, “True-motion estimation with 3-D recursive search block matching,” IEEE Trans. Circuits Syst. Video Technol., vol. 3, no. 5, pp. 368–379, Oct. 1993.
[6] A. Heinrich et al., “Optimization of hierarchical 3DRS motion estimators for picture rate conversion,” IEEE Journal of Selected Topics in Signal Processing, Apr. 2011.
[7] R. Depommier and E. Dubois, “Motion estimation with detection of occlusion areas,” IEEE Int. Conf. Acoustics Speech Signal Processing, pp. III.269–III.272, Mar. 1992.
[8] Serdar Ince and Janusz Konrad, “Geometry-based estimation of occlusions from video frame pairs,“ IEEE Trans. Int. Conf. Acoustics, Speech, and Signal Processing, pp. ii/933 - ii/936 Vol. 2, 2005.
[9] R. Thoma and M. Bierling, “Motion compensating interpolation considering covered and uncovered background,” Signal Processing: Image Communication, pp. 191–212, Feb. 1989.
[10] Shen-Chuan Tai, Ying-Ru Chen, Zheng-Bin Huang, and Chuen-Ching Wang, “A multi-pass true motion estimation scheme with motion vector propagation for frame rate up-conversion applications,” Journal of Display Technol., vol. 4, no. 2, pp. 188–197, June 2008.
[11] Chirag Jain, Sriram Sethuraman ”A low-complexity, motion-robust, spatio-temporally adaptive video de-noiser with in-loop noise estimation,” IEEE Trans. Image Processing, pp 557-560, 2008.
[12] F. Jin , P. Fieguth , L. Winger, and E. Jernigan, “Adaptive wiener filtering of noisy images and image sequences,” IEEE Int. Conf. Image Processing, pp.349, 2003
[13] J. S. Lee, “Digital image enhancement and noise filtering by use of local statistics,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 2, pp. 165–168, 1980.
[14] V. Zlokolica, A. Pizurica, and W. Philips, “Wavelet-Domain Video Denoising Based on Reliability Measures,” IEEE Journals Trans. Circuits and Systems for Video Technology, vol. 16, no. 8, pp. 993-1007, 2006.
[15] V. Zlokolica, A. Pizurica, and W. Philips, “Video denoising using multiple class averaging with multiresolution,” in Proc. Int. Workshop Visual Content Processing and Representation, Madrid, Spain, pp. 172–179, Sep. 2003.
[16] A. Pizurica, V. Zlokolica, and W. Philips, “Noise reduction in video sequences using wavelet-domain and temporal filtering,” in Proc. SPIE Conf. Wavelet Applicat. Industrial Processing, Providence, RI, pp. 48–59, Oct. 2003.
[17] Y. Ninomiya and Y. Ohtsuka, “A motion-compensated interframe coding scheme for television pictures,” IEEE Trans. Commun., vol. COM-30, no. 1, 1982.
[18] R. Srinivasan and K. R. Rao, “Predictive coding based on efficient motion estimation,” IEEE Trans. Commun., vol. COM-33, no. 8, pp. 888-896, 1985.
[19] G. de Haan and H. Huijgen, “New algorithm for motion estimation,” in Proc. 3rd Int. Workshop on HDW and Beyond, Torino, Italy, 1989.
[20] J. N. Driessen, L. Boroczki, and J. Biemond, “Pel-recursive motion field estimation from image sequences,” J. Viiual Commun. Image Represent., 1991.
[21] J. N. Driessen, L. Boroczki, and J. Biemond, “Pel-recursive motion field estimation from image sequences,” J. Viiual Commun. Image Represent., 1991.
[22] J. N. Driessen, “Motion estimation for digital video,” Ph.D. dissertation, Delft Univ. of Tech., Sept. 1992
[23] G. de Haan, “Motion estimation and compensation,” Ph.D. dissertation, Delft Univ. Tech., Sept. 1992.
[24] G. de Haan and H. Huijgen, “Video image motion vector estimation with asymmetric update region,” European Patent Application EP-A 0 474 276, European Patent Bulletin, no. 1992.
[25] Y. Wang, J. Ostermann, Y. Q. Zhang, “Video Processing and Communications,” Prentice-Hall, pp. 609, 2002.
[26] A. Bovik, “Handbook of Image and Video Processing,” Academic Press Series in Communications, Networking, and Multimedia, pp. 891, 2000.
[27] C. Harris and M. Stephens, “A Combined Corner and Edge Detector,” Proc. Alvey Vision Conf., pp. 147-151, 1988.
[28] D.L. Donoho and I.M. Johnstone, “Ideal spatial adaption by wavelet shrinkage,” Biometrika, vol. 8, pp. 425.455, 1994.
[29] V. Zlokolica, A. Pizurica, and W. Philips, “Wavelet domain noise-robust motion estimation and noise estimation for video denoising,” presented at the 1st Int. Workshop Video Processing Quality Metrics Consum. Electron., Scotssdale, AZ, Paper no. 200, Jan. 2005.
[30] J. Immerkaer, “Fast noise variance estimation,” Computer Vision and Image Understanding, vol. 64, pp. 300–302, September 1996.
[31] A.-M. Huang and T. Nguyen, “Correlation-Based Motion Vector Processing With Adaptive Interpolation Scheme for Motion-Compensated Frame Interpolation,” IEEE Trans. Image Processing, Volume.18, Issue.4, pp.740, 2009
[32] H. Y. Cheong, A. M. Tourapis, J. Llach, and J. Boyce, “Adaptive spatio-temporal filtering for video denoising,” IEEE Int. Conf. Image Processing, Singapore, pp. 965–968, 2004.
[33] F. Cocchia, S. Carrato, and G. Ramponi, “Design and real-time implementation of a 3-D rational filter for edge preserving smoothing,” IEEE Trans. Consum. Electron., vol. 43, no. 4, pp. 1291–1300, Nov. 1997.