Comparative Analysis of Diamond Search and its Star Refinement Algorithms for Motion Estimation

Satish Kumar Sahu*, Dolley Shukla**
* M.E. Student, Department of Electronics Telecommunication Engineering, SSTC, SSGI-FET, Junwani, Bhilai, Durg, India.
Associate Professor, Department of Information Technology, SSTC, SSGI, FET, Junwani, Bhilai, Durg, India
Periodicity:April - June'2017


Motion estimation is a fundamental procedure for video compression. It is directly related to the compression efficiency by reducing temporal redundancies. Motion estimation is the most critical part of a video encoder and 50% coding complexity or computational time depends on it. To minimize the computational time, there were various ME algorithms proposed and implemented. In this paper, the authors provide performance analysis of Star refinement on Diamond search algorithm and after evaluation, they determine the most optimal algorithm. Each algorithm is evaluated using many test videos and compared through Peak Signal to Noise Ratio (PSNR) and per macro block search points (i.e. computation time) along with search areas. Results suggest that among all the evaluated algorithms, Star Diamond- Diamond Search has the best PSNR based on computation time.


Diamond Search, Star Diamond Search, Star Diamond-Diamond Search, Motion Vector, Motion Estimation.

How to Cite this Article?

Sahu, S.K. and Shukla D. (2017). Comparative Analysis of Diamond Search and its Star Refinement Algorithms for Motion Estimation. i-manager’s Journal on Image Processing, 4(2), 16-21.


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