Abstract
A Fractal based Neural Network Radial Basis Function (FNNRBF) for image compression is proposed through this work. Generally, a large amount of data are required to represent digital images where the transmission and storage of such images are time consuming and unrealizable. Hence, image compression technique can be used to reduce the storage and transmission costs. In order to overcome the difficulties a Hybrid Fractal with NNRBF image compression techniques FNNRBF is proposed. The implementation of this technique shows the effectiveness in terms of compression of medical images. Also, a comparative synthesis is performed to prove that the proposed system is capable of compressing the images effectively in terms of Compression Ratio (CR), Peak Signal to Noise Ratio (PSNR) and memory space.
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References
Alexandridis, A., Eva, C., Haralambos, S.: Radial basis function network training using a nonsymmetric partition of the input space and particle swarm optimization. IEEE Trans. Neural Netw. Learn. Syst. 24(2), 219–230 (2013)
Arif, O., Patricio, A.V.: Kernel map compression using generalized radial basis functions. In: Proceedings of 2009 IEEE 12th International Conference on Computer Vision, Kyoto, pp. 1119–1124 (2009)
Cheng, K., Jeffrey, D.: Lossless to lossy dual-tree BEZW compression for hyperspectral images. IEEE Trans. Geosci. Remote Sens. 52(9), 5765–5770 (2014)
Ding, J.J., Hsin-Hui, C., Wei-Yi, W.: Adaptive Golomb code for joint geometrically distributed data and its application in image coding. IEEE Trans. Circuits Syst. Video Technol. 23(4), 661–670 (2013)
Ghrare, S.E., Ahmed, R.K.: Digital image compression using block truncation coding and Walsh Hadamard transform hybrid technique. In: Proceedings of 2014 International Conference on Computer, Communications, and Control Technology (I4CT), Langkawi, pp. 477–480 (2014)
Hormat, A.M., Rostami, V., Shokoohi, Z., Habibi, H.: Fuzzy modified forward-only counter propagation network to improve image compression. In: Proceedings of 2013 3rd Joint Conference of AI & Robotics and 5th RoboCup Iran Open International Symposium, Tehran, pp. 1–6 (2013)
Jiang, C., Shuxin, Y.: A hybrid image compression algorithm based on human visual system. In: IEEE International Conference on Computer Application and System Modeling (ICCASM), vol. 9, pp. 170–173 (2010)
Karayiannis, N.B., Mary, M.R.G.: On the construction and training of reformulated radial basis function neural networks. IEEE Trans. Neural Networks 14(4), 835–846 (2003)
Kekre, H.B., TanujaSarode, P.: Image compression based on hybrid wavelet transform generated using orthogonal component transforms of different sizes. Int. J. Soft Comput. Eng. 3(3), 53–57 (2013)
MohammedHasan, T., Xingqian, W.: An adaptive fractal image compression. IJCSI Int. J. Comput. Sci. 10(2), 98–110 (2013)
Niros, A.D., Tsekouras, G.E.: A novel training algorithm for RBF neural network using a hybrid fuzzy clustering approach. Fuzzy Sets Syst. 193, 62–84 (2012)
Perumal, B., Pallikonda Rajasekaran, M.: Efficient image compression techniques for compressing multimodal medical images using neural network radial basis function approach. Int. J. Imaging Syst. Technol. 25(2), 115–122 (2015)
Rawat, C., Sukadev, M.: A hybrid image compression scheme using DCT and fractal image compression. Int. Arab J. Inf. Technol. 10(6), 553–562 (2013)
Seeli, D.S., Jeyakumar, M.K.: A study on fractal image compression using soft computing techniques. IJCSI Int. J. Comput. Sci. 9(6), 420–430 (2012)
Sridhar, S., Kumar, P.R., Ramanaiah, K.V., Nataraj, D.: Coiflets, artificial neural networks and predictive coding based hybrid image compression methodology. In: 2014 2nd International Conference on Devices, Circuits and Systems (ICDCS), Combiatore, pp. 1–6 (2014)
Thakur, S., Nilesh, K.D., Kavita, T.: A highly efficient gray image compression codec using neuro fuzzy based soft hybrid JPEG standard. In: Proceedings of Second International Conference, Emerging Research in Computing, Information, Communication and Applications, vol. 1, pp. 625–631 (2014)
Vascan, O., Ionel-Bujorel, P.: Image compression using radial basis function networks. Telecommun. Control 5, 195–198 (2013)
Zhang, C., Xiaofei, H.: Image compression by learning to minimize the total error. IEEE Trans. Circuits Syst. Video Technol. 23(4), 565–576 (2013)
Acknowledgement
The author would like [9] to thank the Sir.C.V. RAMAN KRISHNAN International Research Center for providing financial assistance under the University Research Fellowship. Also we thank the Department of Electronics and Communication Engineering of Kalasalingam University, (Kalasalingam Academy of Research and Education), Tamil Nadu, India for permitting to use the computational facilities available in Centre for Research in Signal Processing and VLSI Design which was setup with the support of the Department of Science and Technology (DST), New Delhi under FIST Program in 2013 (Reference No: SR/FST/ETI-336/2013 dated November 2013).
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Perumal, B., Pallikonda Rajasekaran, M., Arun Prasath, T. (2017). Efficient Hybrid Approach for Compression of Multi Modal Medical Images. In: Arumugam, S., Bagga, J., Beineke, L., Panda, B. (eds) Theoretical Computer Science and Discrete Mathematics. ICTCSDM 2016. Lecture Notes in Computer Science(), vol 10398. Springer, Cham. https://doi.org/10.1007/978-3-319-64419-6_33
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DOI: https://doi.org/10.1007/978-3-319-64419-6_33
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