Progressive Motion Vector Clustering for Motion Estimation and Auxiliary Tracking

2015 ◽  
Vol 11 (3) ◽  
pp. 1-23 ◽  
Author(s):  
Ke Chen ◽  
Zhong Zhou ◽  
Wei Wu
Keyword(s):  
Motion Estimation ◽  
Motion Vector ◽  
Progressive Motion ◽  
Vector Clustering

Improved global motion estimation via motion vector clustering for video stabilization

2016 ◽  
Vol 54 ◽  
pp. 39-48 ◽  
Author(s):  
Bo-Hao Chen ◽  
Andrey Kopylov ◽  
Shih-Chia Huang ◽  
Oleg Seredin ◽  
Roman Karpov ◽  
...  
Keyword(s):  
Motion Estimation ◽  
Motion Vector ◽  
Video Stabilization ◽  
Global Motion ◽  
Global Motion Estimation ◽  
Vector Clustering

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.

scholarly journals A Fast Fractal Video Coding Algorithm Using Cross-Hexagon Search for Block Motion Estimation

2011 ◽  
Vol 2011 ◽  
pp. 1-10 ◽  
Author(s):  
Kamel Belloulata ◽  
Shiping Zhu ◽  
Zaikuo Wang
Keyword(s):  
Motion Estimation ◽  
Video Coding ◽  
Search Algorithm ◽  
Motion Vector ◽  
Block Matching ◽  
Block Motion ◽  
Search Patterns ◽  
Vector Distribution ◽  
Search Speed ◽  
Block Motion Estimation

We propose a novel fractal video coding method using fast block-matching motion estimation to overcome the drawback of the time-consuming character in the fractal coding. As fractal encoding essentially spends most time on the search for the best-matching block in a large domain pool, search patterns and the center-biased characteristics of motion vector distribution have large impact on both search speed and quality of block motion estimation. In this paper, firstly, we propose a new hexagon search algorithm (NHEXS), and, secondly, we ameliorate, by using this NHEXS, the traditional CPM/NCIM, which is based on Fisher's quadtree partition. This NHEXS uses two cross-shaped search patterns as the first two initial steps and large/small hexagon-shaped patterns as the subsequent steps for fast block motion estimation (BME). NHEXS employs halfway stop technique to achieve significant speedup on sequences with stationary and quasistationary blocks. To further reduce the computational complexity, NHEXS employs modified partial distortion criterion (MPDC). Experimental results indicate that the proposed algorithm spends less encoding time and achieves higher compression ratio and compression quality compared with the traditional CPM/NCIM method.

The Improve of HFPS Algorithm in AVS Video Coding

2012 ◽  
Vol 457-458 ◽  
pp. 867-871
Author(s):  
Xi Nan Zhang ◽  
Ai Lin Liu
Keyword(s):  
Motion Estimation ◽  
Video Coding ◽  
Video Sequence ◽  
Search Algorithm ◽  
Motion Vector ◽  
Video Sequences ◽  
The Law ◽  
Motion Characteristics ◽  
Algorithm Search ◽  
Improved Algorithm

To reduce the complexity of AVS sub-pixel motion vector search, this paper proposes a new improved algorithm based on little diamond window searching policies according to the law of the general video sequence motion vector focusing on the near of the initial searching point at the time of the subpixel motion estimation on the basis of deeping analysis to HFPS algorithm. Comparing with the sub-pixel HFPS algorithm search algorithm, the time of sub-pixel motion estimation can reduce 51.97％ on average and efficiently decrease the computational number of sub-pixel motion estimation on average PSNR lost less than 0.01dB to the video sequences with different motion characteristics.

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