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Reseach Article

Block-based Motion Estimation in Video Frames using Artificial Neural Networks: A Selective Review

by Krishna Kumar, Krishan Kumar, Rahul Mishra
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 137 - Number 1
Year of Publication: 2016
Authors: Krishna Kumar, Krishan Kumar, Rahul Mishra
10.5120/ijca2016908668

Krishna Kumar, Krishan Kumar, Rahul Mishra . Block-based Motion Estimation in Video Frames using Artificial Neural Networks: A Selective Review. International Journal of Computer Applications. 137, 1 ( March 2016), 27-32. DOI=10.5120/ijca2016908668

@article{ 10.5120/ijca2016908668,
author = { Krishna Kumar, Krishan Kumar, Rahul Mishra },
title = { Block-based Motion Estimation in Video Frames using Artificial Neural Networks: A Selective Review },
journal = { International Journal of Computer Applications },
issue_date = { March 2016 },
volume = { 137 },
number = { 1 },
month = { March },
year = { 2016 },
issn = { 0975-8887 },
pages = { 27-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume137/number1/24241-2016908668/ },
doi = { 10.5120/ijca2016908668 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:37:11.702493+05:30
%A Krishna Kumar
%A Krishan Kumar
%A Rahul Mishra
%T Block-based Motion Estimation in Video Frames using Artificial Neural Networks: A Selective Review
%J International Journal of Computer Applications
%@ 0975-8887
%V 137
%N 1
%P 27-32
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Nowadays, we are very frequently transmitting the video over internet. This is due to an extensive increase in multimedia applications over hand held devices, such as smart mobile phones and also other advance conventional devices. Motion Estimation is an important field of study in the area of motion analysis and motion compression. The motion estimation is done by using two basic approaches, namely, pixel-based motion estimation and block-based motion estimation. Here we have proposed a detailed study literature survey and review of the block-based estimation methods in detail. This paper presents a comprehensive review of block based motion estimation techniques which plays a vital role in multimedia transmission over public network. The advantage of this review paper is to find the absolute optimal solution.

References
  1. Emmanuel Reusens, Touradj Ebrahimi and Murat Kunt, “Dynamic coding of visual information”, IEEE Transactions on Circuit and Systems for Video Technology, vol. 7, no. 3, 1997, pp. 489-500.
  2. Detlev Marpe, Thomas Wiegand and Gary J. Sullivan, “The H.264/MPEG4 advanced video coding standard and its applications”, IEEE Communications Magazine, 2006, pp. 134-143.
  3. Karel Rijkse and KPN Research, "H.263: Video coding for low bit rate communication", IEEE Cominunications Magazine, 1996, pp. 42-45.
  4. Thomas Sikora, “The MPEG-4 video standard verification model", IEEE Transactions on Circuit and Systems for Video Technology, vol. 7, no. 1, 1997, pp. 19-31.
  5. Borko Furht, Joshua Greenberg and Raymond Westwater, “Motion estimation algorithm for video compression”, Kluwer Academic Publishers, Norwell, MA, 1997, Ch. 2 and 3.
  6. Iain E.G. Richardson, “Video Codec Design”, John Wiley and Sons Ltd., 2002, Ch. 4, 5 and 6.
  7. J. Ribas-Corbera and D.L. Neuhoff, "On the optimal block size for block-based motion compensated video coders", SPIE Proceedings of Visual Communications and Image Processing, vol. 3024, 1997, pp. 1132-1143.
  8. Y. Wang and Q. Zhu, “Error control and concealment for video communication: a review”, Proceedings of the IEEE, special issue on Multimedia Signal Processing. vol. 86, 1998, pp. 974-997.
  9. H. G. Musmann, P. Pirsch and H. J. Grallert, “Advances in picture coding”, IEEE Proceeding of video coding, vol. 73, no. 4, 1985, pp. 523-548.
  10. M. J. Chen, L. G. Chen, T. D. Chiues, and Y. P. Lee, “A new block matching criterion for motion estimation and its implementation”, IEEE Transaction on Circuits System Video Technology, vol. 5, no. 6, 1995, pp. 231-236.
  11. Huynh-Thu and Ghanbari, M., "Scope of validity of PSNR in image/video quality assessment", Electronics Letters, vol. 44, no. 13, 2008, pp. 800–801.
  12. J.R. Jain and A.K. Jain, "Displacement measurement and its application in interframe image coding", IEEE Transaction on Communication, vol. COM-29, no. 12, 1981, pp. 1799-1808.
  13. Steven L. Kilthau, Mark S. Drew and Torsten Moller, “Full search content independent block matching based on Fast Forier Transformation”, In Proceeding of the IEEE conference on ICIP, 2002, pp. 669-672.
  14. M.H. Chan, Y.B. Yu and A.G. Constantinides, "Variable size block matching motion compensation with applications to video coding", IEE Proceedings of advance in video coding, vol. 137, no. 4, 1990, pp. 205-212.
  15. R.A. Packwood, M.K. Steliaros and G.R. Martin, "Variable size block matching motion compensation for object-based video coding", IEEE 6th International Conference on Image Processing and its Applications, Dublin, Ireland, 1997, pp. 56-60.
  16. Siwei Ma, Wen Gao and Yan Lu, “Rate-Distortion analysis for H.264/AVC video coding and its application to rate control”, IEEE Transaction on Circuits and Systesm for Video Technology, vol. 15, no. 12, 2005, pp. 1533-1544.
  17. T. Koga, K. Iinuma, A. Hirano, Y. Iijima and T. Ishiguro, “Motion compensated interframe coding for video conferencing”, Proc. Nat. Telecommunication Conference. New Orleans, LA, 1981, pp. G5.3.1–G5.3.5.
  18. Li Renxiang, Bing Zeng and Ming L. Liou, “A New Three-Step search algorithm for block motion estimation”, IEEE Transactions on Circuits and System for Video Technology, vol. 4, no. 4, 1994, pp. 438-442.
  19. Jianhua Lu and Ming L. Liou, “A Simple and Efficent search algorithem for block matching motion estimation”, IEEE Transactions on Circuits and System for Video Technology, vol. 7, no. 2, 1997, pp. 429-433.
  20. Lai-Man Po and Wing-Chaungh Ma, “A Novel Four-Step search algorithm for block matching motion estimation”, IEEE Transactions on Circuits and System for Video Technology, vol. 6, no. 3, 1996, pp. 313-317.
  21. Shan Zhu and Kai-Kuang Ma, “A New Diamond search for fast block matching motion estimation”, IEEE Transactions on Image Processing, vol. 9, no. 2, 2000, pp. 287-290.
  22. You Nie and Kai-Kuang Ma, “Adaptive-Rood Pattern search for fast black matching motion estimation”, IEEE Transactions on Image Processing, vol. 11, no. 12, 2002, pp. 1442-1448.
  23. Martin Slanin and Vaclav Ricny, “Estimating PSNR in high definition H.264/AVC video sequances using artificial neural networks”, IEEE Radioengineeing, vol. 17, no. 3, 2008, pp. 103-108.
  24. Pascal Flewy and Olivier Egger, “Neural network based image coding quality prediction”, In Proceeding of the ICASSP 1997, Munich, pp. 3413-3416.
  25. Yong Zhang and Ming Ming Zhang, “Application of artificial neural network in video compression coding”, IEEE International Conference on Information Management, Innovation Management and Industrial Engineering 2008, pp. 207-210.
  26. Rishabh Malhotra and Kunio Takaya, “A novel approach for finding the movement of an object in video sequances by an artificial neural network for 2.5D object modeling”, In proceeding of the IEEE, CCECE/CCGI, Saskatoon, 2005, pp. 984-987.
  27. A. Gersho and R.M. Gray, “Vector Quantization and Signal Compression”, Kluwer Academic Publisher, 1991.
  28. Mohamed Berkane and Patrick Clarysse, “A neural network-based approach to motion estimation with discontinuities”, IEEE Proceeding of the Eighth International Conference on Hybrid Intelligent Systems, 2008, pp. 356-361.
  29. Dae-Hyun Ryu, “Block matching algorithm using neural network”, IEEE TENCON - Speech and Image Technologies for Computing and Telecommunications, 1997, pp. 379-382.
  30. K. Ramchandran, A. Ortega and M. Vetterli, “Bit allocation for dependent quantization with applications to multiresolution and MPEG video coders”, IEEE Transaction Image Processing, vol. 3, no. 5, 1994, pp. 533-545.
  31. Sabir M.F., Heath R.W. and Bovik A.C., “Joint source-channel distortion modeling for MPEG-4 video”, IEEE Transactions on Image Processing, vol. 18, no. 1, 2009, pp. 90 – 105.
  32. Vikas Sagar, Dr. Krishan Kumar, “A Symmetric Key Cryptography using Genetic Algorithm and Error Back Propagation Neural Network” 9th INDIACom; INDIACom-2015 2015 2nd International Conference on “Computing for Sustainable Global Development”, 11th – 13th March, 2015 Bharati Vidyapeeth’s Institute of Computer Applications and Management (BVICAM), New Delhi (INDIA).
  33. Vikas Sagar, Dr. Krishan Kumar, “A Symmetric Key Cryptographic Algorithm Using Counter Propagation Network (CPN)” ICTCS '14, November 14 - 16 2014, Udaipur, Rajasthan, India Copyright 2014 ACM.
  34. Sanjeev Kumar, Krishan Kumar and Anand Kumar Pandey. Article: A Comparative Study of Call Admission Control in Mobile Multimedia Networks using Soft Computing. International Journal of Computer Applications 107(16):5-11, December 2014.
  35. Sanjeev Kumar, Krishan Kumar, Pramod Kumar, “Mobility Based Call admission Control and Resource Estmation in Mobile Multimedia Networks using Artificial Neural Networks”, IEEE International Conference, NGCT, Petroleum University, Dehradun, 4-5 September, 2015.
  36. Mohit Mittal and Krishan Kumar. Article: Quality of Services Provisioning in Wireless Sensor Networks using Artificial Neural Network: A Survey. International Journal of Computer Applications 117(5):28-40, May 2015
Index Terms

Computer Science
Information Sciences

Keywords

Motion estimation video compression motion Vectors.