scholarly journals Modeling of Video Sequences by Gaussian Mixture: Application in Motion Estimation by Block Matching Method

2010 ◽  
Vol 2010 (1) ◽  
Author(s):  
Abdenaceur Boudlal ◽  
Benayad Nsiri ◽  
Driss Aboutajdine
Keyword(s):  
Motion Estimation ◽  
Gaussian Mixture ◽  
Block Matching ◽  
Video Sequences ◽  
Matching Method

scholarly journals An improved block matching algorithm for motion estimation in video sequences and application in robotics

2018 ◽  
Vol 68 ◽  
pp. 92-106 ◽  
Author(s):  
Kamanasish Bhattacharjee ◽  
Sushil Kumar ◽  
Hari Mohan Pandey ◽  
Millie Pant ◽  
David Windridge ◽  
...  
Keyword(s):  
Motion Estimation ◽  
Block Matching ◽  
Video Sequences ◽  
Matching Algorithm ◽  
Block Matching Algorithm

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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