Abstract
With the advent of multimedia technology, video compression has become imperative. The high definition of video required huge amount of storage space and large amount of bandwidth for the transmission of video. The largest part of multimedia is video. There is upsurge in demand of compressed data due to excessive usage of multimedia applications on Internet. Hence in success of multimedia data, video compression and decompression are majorly used. There are already various transform functions such as wavelet transform, DCT transform and fractal transform functions which are used for compression and decompression of video. In all transform function, the fractal transforms function adhere to the rule of block symmetry. It is very proficient process, but the rate of compression is very time-consuming, however, the decompression is very fast. In this paper, we adopted fractal triangular partitioning scheme to compress and decompress the videos. Here in this work for our analysis, we used very short length videos which are of different size. The primary objective of this work is to minimize the encoding time of video and achieve better compression ratio. The process of video compression and decompression methods is simulated in MATLAB software and used some standard parameters for the evaluation of compression and decompression results.
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References
Minas A, Sediq H (2013) Compression of an AVI video file using fractal system. Int J Comput Sci Issues 10(2):182–189
Yuanfang X, Xia S (2014) System design for real-time image processing based on multicore DSP. J Netw 9(11):3143–3150
Alarabeyyat A, Al-Hashemi S, Khdour T, Hjouj Btoush M, Bani-Ahmad S, Al-Hashemi R (2012) Lossless image compression technique using combination methods. J Softw Eng Appl 5(12):752–763
Kulkarni M, Kulkarni D (2017) Analysis of fractal inter frame video coding using parallel approach. J Sign, Image Video Process 11(4):629–634
Dhok S, Deshmukh R, Keskar A (2012) Efficient fractal image coding using fast Fourier transform. GSTF Int J Comput 1(2):35–40
Gao H, Zeng W, Chen J, Zhang C (2016) An improved fast fractal image compression coding method. Rev Téc Ing Univ Zulia 39(9):241–247
Lima V, Robson W, Pedrini H (2013) 3D searchless fractal video encoding at low bit rates. J Math Imaging Vision 45(3):239–250
Kamble S, Thakur N, Bajaj P (2016) Review on block matching motion estimation and automata theory based approaches for fractal coding. Int J Interact Multimedia Artif Intell 4(2):91–104
Aswani R, Kamble S (2014) Fractal video compression using block matching motion estimation—a study. IOSR J VLSI Sign Process 4(2):82–90
Sudhkar R, Letitia S (2015) Motion estimation scheme for video coding using hybrid discrete cosine transform and modified unsymmetrical-cross multi hexagon grid search algorithm. Middle-East J Sci Res 23:848–855
Pandit S, Shukla P, Tiwari A (2018) Fractal compression of an AVI video file using DWT and particle swarm optimization. Int J Comput Sci Inf Secur 16(1):128–131
Pandit S, Shukla P, Tiwari A (2018) A proficient video compression method based on DWT and HV partition fractal transform function. Int J Sci Eng Technol 7(2):20–24
Pandit S, Shukla P, Tiwari A (2018) Enhance the performance of video compression based on fractal H-V partition technique with particle swarm optimization. Int J Comput Sci Eng 6(1):31–35
Andonova S, Popovic D (1994) Video coding using fractal based image compression. In: IEEE international conference on systems, man and cybernetics, pp 343–348
Zhu S, Li L, Chen J, Belloulata K (2014) An automatic region-based video sequence codec based on fractal compression. AEU-Int J Electron Commun 68(8):795–805
Jacquin A (1992) Image coding based on a fractal theory of iterated contractive image transformations. IEEE Trans Image Process 1(1):18–30
Fisher Y (1995) Fractal image compression: theory and application. Springer, New York
Huang Z (2017) Frame-groups based fractal video compression and its parallel implementation in Hadoop cloud computing environment. Int J Multi-dimension Syst Sign Process 29(100):1–18
Ponlatha S, Sabeenian R (2014) An artificial neural network based lossless video compression using multilevel snapshots and wavelet transform using intensity measures. Int J Eng Technol 6(4):1900–1908
Wu Z, Yan B (2010) An effective fractal image compression algorithm. In: Proceeding of IEEE international conference on computer application and system modeling, pp 139–143
Peng H, Wang M, Lai C (2007) Design of parallel algorithms for fractal video compression. Int J Compute Math 84(2):193–202
Distasi R, Nappi M, Riccio D (2006) A range/domain approximation error based approach for fractal image compression. IEEE Trans Image Process 15(1):89–97
Wang M, Lai C (2007) Grey video compression methods using fractals. Int J Comput Math 84(11):1555–1566
Zhao E, Dan L (2005) Fractal image compression methodology: a review. In: International conference on information, technology and applications, pp 756–759
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Pandit, S., Shukla, P.K., Tiwari, A. (2019). Investigating the Effect of Compression and Decompression in Video Using Fractal Technique. In: Peng, SL., Dey, N., Bundele, M. (eds) Computing and Network Sustainability. Lecture Notes in Networks and Systems, vol 75. Springer, Singapore. https://doi.org/10.1007/978-981-13-7150-9_8
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DOI: https://doi.org/10.1007/978-981-13-7150-9_8
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