A multiresolution block matching technique for image motion estimation

1995 ◽  
Author(s):  
I. Rhee
Keyword(s):  
Motion Estimation ◽  
Block Matching ◽  
Image Motion ◽  
Matching Technique

Motion Model Parameters Estimation for Electronic Image Stabilization Instrument

2013 ◽  
Vol 325-326 ◽  
pp. 1543-1546
Author(s):  
Xun Yu Zhong ◽  
Tian Hui Ren
Keyword(s):  
Motion Estimation ◽  
Least Squares Method ◽  
Estimation Method ◽  
Optimal Estimation ◽  
Block Matching ◽  
Parameters Estimation ◽  
Transformation Model ◽  
Image Motion ◽  
Model Parameters ◽  
Image Stabilization

Fast and optimal motion estimation method is proposed for electronic image stabilization. First, an approach for macro-block judgment is presented. Before motion vectors calculation, gradient information is analyzed, only useful reference blocks that are indispensable for accurate motion estimation are selected, by which the number of macro-blocks for subsequent calculation is reduced. Second, in the block matching, an improved SSDA is used to reduce computing cost. Finally, the affine transformation model and similarity transformation model of image motion are created and using least squares method for solving the optimal estimation of model parameters. Experimental results show the accuracy and fast computing speed of the proposed method.

Model Parameters Estimation for Image Motion Compensation

2013 ◽  
Vol 347-350 ◽  
pp. 3672-3676
Author(s):  
Xun Yu Zhong ◽  
Xiao Shan Li ◽  
Yong Gang Zhao
Keyword(s):  
Motion Estimation ◽  
Least Squares Method ◽  
Estimation Method ◽  
Optimal Estimation ◽  
Block Matching ◽  
Parameters Estimation ◽  
Transformation Model ◽  
Image Motion ◽  
Model Parameters ◽  
Macro Block

Fast and optimal motion estimation method is proposed for electronic image stabilization. First, an approach for macro-block judgment is presented. Before motion vectors calculation, gradient information is analyzed, only useful reference blocks that are indispensable for accurate motion estimation are selected, by which the number of macro-blocks for subsequent calculation is reduced. Second, in the block matching, an improved SSDA is used to reduce computing cost. Finally, the affine transformation model and similarity transformation model of image motion are created and using least squares method for solving the optimal estimation of model parameters. Experimental results show the accuracy and fast computing speed of the proposed method.

The application of improved block-matching method and block search method for the image motion estimation

2010 ◽  
Vol 283 (23) ◽  
pp. 4619-4625 ◽  
Author(s):  
Fei Yu ◽  
Mei Hui ◽  
Wei Han ◽  
Peng Wang ◽  
Li-quan Dong ◽  
...  
Keyword(s):  
Motion Estimation ◽  
Search Method ◽  
Block Matching ◽  
Image Motion ◽  
Matching Method

Adaptive Return Prediction Block Matching Algorithms for Video Coding

2011 ◽  
Vol 145 ◽  
pp. 277-281
Author(s):  
Vaci Istanda ◽  
Tsong Yi Chen ◽  
Wan Chun Lee ◽  
Yuan Chen Liu ◽  
Wen Yen Chen
Keyword(s):  
Motion Estimation ◽  
Video Compression ◽  
Web Site ◽  
Motion Vector ◽  
Temporal Correlation ◽  
Block Matching ◽  
Asynchronous Learning ◽  
Learning And Teaching ◽  
Block Motion ◽  
Reconstruction Image

As the development of network learning, video compression is important for both data transmission and storage, especially in a digit channel. In this paper, we present the return prediction search (RPS) algorithm for block motion estimation. The proposed algorithm exploits the temporal correlation and characteristic of returning origin to obtain one or two predictive motion vector and selects one motion vector, which presents better result, to be the initial search center. In addition, we utilize the center-biased block matching algorithms to refine the final motion vector. Moreover, we used adaptive threshold technique to reduce the computational complexity in motion estimation. Experimental results show that RPS algorithm combined with 4SS, BBGDS, and UCBDS effectively improves the performance in terms of mean-square error measure with less average searching points. On the other hand, accelerated RPS (ARPS) algorithm takes only 38% of the searching computations than 3SS algorithm, and the reconstruction image quality of the ARPS algorithm is superior to 3SS algorithm about 0.30dB in average overall test sequences. In addition, we create an asynchronous learning environment which provides students and instructors flexibility in learning and teaching activities. The purpose of this web site is to teach and display our researchable results. Therefore, we believe this web site is one of the keys to help the modern student achieve mastery of complex Motion Estimation.

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