A New Pentagon Search Algorithm for Fast Block-Matching Motion Estimation
Neetish Kumar1, Deepa Raj2
1Neetish Kumar, Department of Computer Science, BBA University (A Central University), Lucknow, India.
2Deepa Raj, Department of Computer Science, BBA University (A Central University), Lucknow, India.
Manuscript received on 11 August 2019 | Revised Manuscript received on 14 August 2019 | Manuscript published on 30 August 2019 | PP: 4125-4130 | Volume-8 Issue-10, August 2019 | Retrieval Number: J98630881019/2019©BEIESP | DOI: 10.35940/ijitee.J9863.0881019
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The impact of search pattern is a crucial part in the block-based motion estimation for finding the motion vector. An issue of distortion performance and search speed heavily depends upon the size and shape of search strategy applied. Performing the deep analysis for motion vector distribution on standard test videos, it is desirable to have such type of algorithm that meets the requirement of searching motion vector in less time. Hence, a new kind of Pentagon algorithm is proposed in this paper for fast block-matching motion estimation (BMME). It is an easy and efficient technique for finding motion vector. Experimental results expose the proposed Pentagon algorithm sparsely surpass the noted Diamond search (DS) algorithm. The new Pentagon search algorithm is examined with the previously proposed Diamond search algorithm in terms of performance measure; the proposed algorithm attains better performance with the less complexity. The experimental examination also depicts that the pentagon algorithm is better than the previously proposed Diamond search (DS) in terms of mean-square error performance and required the number of search points. The overall speed improvement rate (SIR) is about 31% with respect to the DS.
Keywords: Diamond Search, Motion Estimation, PSNR, Search Points.
Scope of the Article: Web Algorithms