Abstract
Increase in demand for better appearance and less storage requirement of an image has led to explore various image compression techniques. Due to technological advancement of photo capturing devices such as single-lens reflex camera (SLR), digital SLR, smart phone cameras, and satellite sensors, more detailed information can be recorded in a single image. A coloured image captured by high wavelength sensors produces large-sized image as it contains highly correlated data. Many image compression and analysis techniques have been developed to aid the interpretation of images and to compress as much information as possible in it. The goal of image compression is to recreate original image with less number of bits and minimal data loss. For generating computer graphic images and compression of objects, it has been suggested that by storing images in the form of transformation instead of pixels lead to compression and can be achieved through fractal coding. In fractal image compression, encoding image blocks into fractal codes using iterated function system (IFS) takes large amount of time taken to compress it. A study of various meta-heuristics techniques, which are designed to solve complex problems approximately, has been conducted to improve upon computational time of fractal coding as well as compression ratio, while maintaining image visually. In this paper, using the property of pattern adaption of surroundings, cuttlefish optimisation algorithm is applied to minimise the time taken for fractal coding. Compression results have been compared with other meta-heuristic techniques, such as particle swarm optimisation and genetic algorithm, and has shown high compression ratio of approximately 31%.
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References
R.C. Gonzalez, Digital Image Processing (Pearson Education, India, 2009)
S. Dhawan, A review of image compression and comparison of its algorithms. IJECT 2(1) (2011)
B. Pancholi, R. Shah, M. Modi, Tutorial review on existing image compression techniques. Int. J. Eng. Comput. Sci. 3(8), 7882–7889 (2014). ISSN:2319-7242
M. Devi, U. Mehta, A review on various techniques of image compression. Int. J. Eng. Comput. Sci. 5(7), 17127–17129 (2016). ISSN: 2319-7242
M.F. Barnsley, Fractals Everywhere. Morgan Kaufmann Publishers (2000)
K. Manda et al., Population based metaheuristic techniques for solving optimization problems: a selective survey. Int. J. Emerg. Technol. Adv. Eng. 2(11) (2012). ISSN 2250-2459
Y. Xinjie, G. Mitsuo, Introduction to Evolutionary Algorithms (Springer, London Limited, 2010)
S. Sehgal, S. Kumar, M. Hima Bindu, Remotely sensed image thresholding using OTSU and differential evolution approach, in: 2017 7th International Conference on Cloud Computing, Data Science and Engineering–Confluence
P. Sindhuja, P. Ramamoorthy, M. Suresh Kumar, A brief survey on nature inspired algorithms: clever algorithms for optimization. Asian J. Comput. Sci. Technol. 7(1), 27–32 (2018). ISSN: 2249-0701
B. Bani-Eqbal, Speeding up fractal image compression. Proc. SPIE. 2418, 2/1–2/3 (1995). https://doi.org/10.1117/12.204140
D. Vidya, R. Parthasarathy, T.C. Bina, N.G. Swaroopa, Architecture for fractal image compression. J. Syst. Archit. 46, 275–1291 (2000)
S.S. Bobde, M.V. Kulkarni, P.V. Kulkarni, Fractal image compression using genetic algorithm, in 2010 International Conference on Advances in Computer Engineering (2010)
M. Omari, S. Yaichi, Image compression based on genetic algorithm optimization (IEEE, 2015). 978–1-4799-8172-4/15
K. Uma, P. Geetha Palanisamy, P. Geetha Poornachandran, Comparison of image compression using GA, ACO and PSO techniques, in IEEE-International Conference on Recent Trends in Information Technology, ICRTIT (2011)
G. Vahdati, H. Khodadadi, M. Yaghoobi, Fractal image compression based on spatial correlation and hybrid particle swarm optimization with genetic algorithm, in 2010 2nd International Conference on Software Technology and Engineering (ICSTE) (2010)
A.S. Eesa, A.M.A. Brifcani, Z. Orman, Cuttlefish algorithm–a novel bio-inspired optimization algorithm. Int. J. Sci. Eng. Res. 4(9) (2013)
A.S. Eesa, A.M.A Brifcani, Z. Orman, A new tool for global optimization problems- cuttlefish algorithm, in Proceeds in International Conference on Computer Science (ICCS 2014) (Rome, Italy, September 18–19, 2014)
S. Binitha, S.S. Sathya, A survey of bio inspired optimization algorithms. Int. J. Soft Comput. Eng. (IJSCE) 2(2) (2012). ISSN: 2231-2307
M. Marcin, S. Czesãaw, Test functions for optimization needs (2005)
J.E. Hutchinson, Fractals and self similarity. Indiana Univ. Math. J. 35(5) (1981)
M.F. Barnsley, A.D. Sloan, A better way to compress images. BYTE 215–218 (1988)
A.E. Jaquin, Image coding based on a fractal theory of iterated contractive image transformation. IEEE Trans. Image Process. 1(1) (1992)
A.E Jaquin, Fractal image coding: a review. Proc. Tile IEEE 81(10) (1993)
Y. Fisher, Fractal Image Compression, Siggraph 92 Course Notes
M.M. Lydia, J.D. Eric, T.H. Roger, N.M. Justin, Mechanisms and behavioural functions of structural coloration in cephalopods. J. R. Soc. Interface (2008)
E. Florey, Ultrastructure and function of cephalopod chromatophores. Am. Zool. (1969)
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Sehgal, S., Ahuja, L., Hima Bindu, M. (2020). Optimising Fractal Encoding Using Cuttlefish Optimisation Algorithm. In: Jain, L., Virvou, M., Piuri, V., Balas, V. (eds) Advances in Bioinformatics, Multimedia, and Electronics Circuits and Signals. Advances in Intelligent Systems and Computing, vol 1064. Springer, Singapore. https://doi.org/10.1007/978-981-15-0339-9_19
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