Open Access   Article

Enhance the Performance of Video Compression Based on Fractal H-V Partition Technique with Particle Swarm Optimization

Shraddha Pandit1 , Piyush Kumar Shukla2 , Akhilesh Tiwari3

1 University Institute of Technology, RGPV, Bhopal, India.
2 Department of Computer Science and Engineering, UIT-RGPV, Bhopal, India.
3 Department of CSE & IT, Madhav Institute of Technology and Science (MITS), Gwalior, India.

Correspondence should be addressed to: svpandit_pict@yahoo.co.in.

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-1 , Page no. 31-35, Jan-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i1.3135

Online published on Jan 31, 2018

Copyright © Shraddha Pandit, Piyush Kumar Shukla, Akhilesh Tiwari . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

View this paper at   Google Scholar | DPI Digital Library

Citation

IEEE Style Citation: Shraddha Pandit, Piyush Kumar Shukla, Akhilesh Tiwari, “Enhance the Performance of Video Compression Based on Fractal H-V Partition Technique with Particle Swarm Optimization”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.1, pp.31-35, 2018.

MLA Style Citation: Shraddha Pandit, Piyush Kumar Shukla, Akhilesh Tiwari "Enhance the Performance of Video Compression Based on Fractal H-V Partition Technique with Particle Swarm Optimization." International Journal of Computer Sciences and Engineering 6.1 (2018): 31-35.

APA Style Citation: Shraddha Pandit, Piyush Kumar Shukla, Akhilesh Tiwari, (2018). Enhance the Performance of Video Compression Based on Fractal H-V Partition Technique with Particle Swarm Optimization. International Journal of Computer Sciences and Engineering, 6(1), 31-35.

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Abstract

The searching of coefficient and blocks in video compression is important phase. For the searching of blocks and coefficient used zig-zag and some other random searching technique for symmetry of blocks. In this paper used particle swarm optimization for the searching of block coefficient in domain and range of fractal transform function. The particle swarm optimization enhances the searching capacity of encoder for the process of compression. The particle swarm optimization decides two dual functions one for the mapping of symmetry and other is mapping of video encoded block. For the process of fractal transform encoding used H-V partition technique. H-V partition technique mapped the data in terms of range and domain for the processing of video compression. The H-V partition process creates multiple rectangle blocks the processing of video. The process of video compression methods simulated in MATLAB software and used some standard parameters for the evaluation of compression results.

Key-Words / Index Term

Video Compression, Fractal Transform, H-V partitioning, MATLAB, MSE

References

[1] Rakhi Ashok Aswani and Prof.S.D.Kamble “Fractal Video Compression using Block Matching Motion Estimation - A Study”, IOSR, 2014, Pp 82-90.
[2] Ehsan Lotfi “A Novel Hybrid System Based on Fractal Coding for Soccer Retrieval from Video Database”, Majlesi Journal of Electrical Engineering, 2012, Pp 40-47.
[3] Nevart A. Minas and Faten H. Mohammed Sediq “Compression of an AVI Video File Using Fractal System”, IJCSI, 2013, Pp 182-189.
[4] R. E. Chaudhari and S. B. Dhok “Review of Fractal Transform based Image and Video Compression”, International Journal of Computer Applications, 2012, Pp 23-31.
[5] Vitor de Lima, William Robson Schwartz and HelioPedrini “3D Searchless Fractal Video Encoding at Low Bit Rates”, J Math Imaging Vis, 2013, Pp 239–250.
[6] Shiping Zhu, Liyun Li, Juqiang Chen and KamelBelloulata “An Efficient Fractal Video Sequences Codec with Multiviews”, Hindawi Publishing Corporation, 2013, Pp 1-9.
[7] Ravindra E. Chaudhari and Sanjay B. Dhok “Fractal Video Coding Using Fast Normalized Covariance Based Similarity Measure”, Hindawi Publishing Corporation, 2016, Pp 1-12.
[8] R. E. Chaudhari and S. B. Dhok “Fast Quadtree Based Normalized Cross Correlation Method for Fractal Video Compression using FFT”, JEET, 2016, Pp 709-718.
[9] MambayeNdiaye, Lisa Terranova, Romain Mallet, Guillaume Mabilleau and Daniel Chappard “Three-dimensional arrangement of b-tricalcium phosphate granules evaluated by microcomputed tomography and fractal analysis”, Acta Biomaterialia, 2015, Pp 404–411.
[10] Shailesh D. Kamble, Nileshsingh V. Thakur and Preeti R. Bajaj “A Review on Block Matching Motion Estimation and Automata Theory based Approaches for Fractal Coding”, IJIMAI, 2016, Pp 91-104.
[11] Shiping Zhu, Liyun Li, Juqiang Chen and KamelBelloulata “An automatic region-based video sequence codec based on fractal compression”, Int. J. Electron. Commun. 2014, Pp 1-12.
[12] KamelBelloulata, Amina Belalia and Shiping Zhu “Object-based stereo video compression using fractals and shape-adaptive DCT”, Int. J. Electron. Commun., 2014, Pp 687–697.
[13] G.Sandhiya, M.Rajkumar and S.G.Vishnu Prasad “Hardware Implementation of 2D - DWT for Video Compression using Bit Parallel Architecture”, IJSETR, 2015, Pp 1211-1215.
[14] Dr. Fadhil Salman Abed and Iraq-Diyala-Jalawla “A Proposed Encoding and Hiding Text in an Image by using Fractal Image Compression”, IJCSE, 2013, Pp 1-13.
[15] Ryan Rey M. Daga and John Paul T. Yusiong “Image Compression Using Harmony Search Algorithm”, IJCSI, 2012, Pp 16-23.
[16] Tanudeep Kaur and Anupam Garg “Review of various Fractal Detection Techniques in X-Ray Images”, IJEDR, 2016, Pp 553-559.
[17] Chun-Ho Wu, W.H. Ip, C. Y. Chan and Kai Leung Yung “A flexible H.264/AVC compressed video watermarking scheme using particle swarm optimization based dither modulation”, International Journal of Electronics and Communications, 2011, Pp 29-36.
[18] Jean-Francois Connolly, Eric Granger and Robert Sabourin “Evolution of heterogeneous ensembles through dynamic particle swarm optimization for video-based face recognition”, Pattern Recognition, 2012, Pp 2460-2477.
[19] J.F. Connolly, E. Granger and R. Sabourin “Incremental adaptation of fuzzy artmap neural networks for video-based face classification”, IEEE, 2009, Pp 1-8.
[20] D.O. Gorodnichy “Video-based framework for face recognition in video”, Conference on Computer and Robot Vision, 2005, Pp 325–344.
[21] Ran Ren, Madan mohan Manokar, Yaogang Shi and Baoyu Zheng “A Fast Block Matching Algorithm for Video Motion Estimation Based on Particle Swarm Optimization and Motion Prejudgment”, IEEE, 2006, Pp 1-5.
[22] Yuanyuan Sun, Rudan Xu, Lina Chen and Xiaopeng Hu “Image compression and encryption scheme using fractal dictionary and Julia set”, IET Image Processing, 2014, Pp 173-183.
[23] 22] D.Sophin Seeli and Dr.M.K.Jeyakumar “A Study on Fractal Image Compression using Soft Computing Techniques ”, IJCSI, 2012, Pp 1-11.