International Journal of Applied Science and Engineering
Published by Chaoyang University of Technology

Sunny Arief Sudiro1*, Aqwam Rosadi Kardian1, Sarifuddin Madenda2, Lingga Hermanto2

1 STMIK Jakarta STI&K Jl. BRI No. 17 Radio Dalam Kebayoran Baru Jakarta Indonesia
2 Gunadarma University Jl. Margonda Raya No. 100 Depok Jawa Barat Indonesia


Download Citation: |
Download PDF


ABSTRACT


Statistical formula processing an image data is commonly used in image processing. In software processing this formula and accessing data stored in memory is an easy task, but in hardware implementation, it is more difficult task due to many of constraints. This article presents hardware implementation of mean & variance statistic formula in effective and efficient way using FGPA Device. The design of circuit for both formulas proposed in this article need only two additions component (in two accumulators) and two shift-right-registers will be used for divisor circuits, one subtractor and one multiplier. In the experiment, processing an image size 8x8 pixels need 64 clocks cycle to conclude the mean & variance calculations. More than 1024 additions component is needed in some design so this design is more efficient.


Keywords: Mean, Variance, FPGA, Accumulator, Counter.


Share this article with your colleagues

 


REFERENCES


  1. Bailey, D.G., Laiber, K.M.J. 2013. Efficient hardware calculation of running statistics. 28th International Conference on Image and Vision Computing New Zealand (IVCNZ 2013), 1–6.

  2. Betkaoui, B., Thomas, D.B., Luk, W., Przulj, N. 2011. A framework for FPGA acceleration of large graph problems: Graphlet counting case study. International Conference on Field-Programmable Technology, 1–8.

  3. Gade, P.B., Khope, S.R. 2016. FPGA based multifocus image fusion using variance Method. Irjet International Research Journal of Engineering and Technology (IRJET), 3.

  4. Irturk, A., Benson, B., Laptev, N., Kastner, R. 2008. FPGA acceleration of mean variance framework for optimal asset allocation. Workshop on High Performance Computational Finance at SC08 International Conference for High Performance Computing, Networking, Storage and Analysis, 1–8.

  5. Ismael, S.F., Mahmood, B.S. 2017. A novel way to design and implement statistical operations based on FPGA. International Journal of Computer Applications (0975–8887), 167.

  6. Kardian, A.R., Sudiro, S.A., Madenda, S. 2016. Efficient implementation of mean formula for image processing using FPGA device. 1st International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), Yogyakarta, Indonesia, ISBN: 978-602-60280-0-6.

  7. Martinez, W.L., Martinez, A.R. 2002. Computational statistics handbook with MATLAB. Chapman & Hall/CRC, USA.

  8. Woods, R.E., Eddins, S.L., Gonzales, R.C. 2005. Digital image processing using MATLAB. Pearson Education.


ARTICLE INFORMATION


Received: 2020-06-05

Accepted: 2020-12-31
Available Online: 2021-03-01


Cite this article:

Sudiro, S.A., Kardian, A.R., Madenda, S., Hermanto, L. 2021. Mean and variance statistic for image processing on FPGA. International Journal of Applied Science and Engineering, 18, 2020115. https://doi.org/10.6703/IJASE.202103_18(1).009

  Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.