Fractal Coding for Texture, Satellite, and Gray Scale Images to Reduce Searching Time and Complexity

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 695)

Abstract

Fractal coding techniques are time-consuming and complex. The proposed Grover’s quantum search algorithm (QSA) reduces the computational complexity in searching mechanism and achieves square root speedup over classical algorithms in an unsorted database. The quantum fidelity can be calculated to reduce minimum matching error between a given range block and its corresponding domain block. The proposed system is implemented for texture, satellite, and grayscale images for different sizes of range and domain blocks. The results are compared and displayed to reduce the complexity in the searching mechanism. The comparative analysis of existing methods and proposed algorithm has been carried out using performance parameters as compression ratio (CR), computational complexity and PSNR.

Keywords

Compression ratio Computational complexity Fractal image compression Grover’s quantum search PSNR Quantum representation 

References

  1. 1.
    Fisher, Y.: Fractal Image Compression (1995)CrossRefGoogle Scholar
  2. 2.
    Chaudhari, R.E., Dhok, S.B.: Wavelet transformed based fast fractal image Comp. In: International Conference on Circuits, Systems, Communications and Information Technology Applications, pp. 64–69 (2014)Google Scholar
  3. 3.
    Kadam, S., Rathod, V.: DCT with quad tree and Huffman coding for color images. Int. J. Comput. Appl. 173(9), 33–37 (2017)Google Scholar
  4. 4.
    Nodehi, A., Mznah, G.S.: Intelligent fuzzy approach for fast fractal image compression. EURASIP J. Adv. Signal Process, 1–9 (2014)Google Scholar
  5. 5.
    Mahalaxmi, G.V.: Implementation of image compression using fractal image compresssion and neural network for MRI images. In: IEEE International Conference on Information Science (ICIS), pp. 60–64 (2016)Google Scholar
  6. 6.
    Al-saidi, N.M.G., Ali, A.H.: Towards enhancing of fractal image compression via block complexity. In: IEEE Annual Conference on New Trends in Information and Communication Technology Applications (NTICT), pp. 246–251 (2017)Google Scholar
  7. 7.
    Abdul, N., Salih, J.: Fractal coding technique based on different block size. In: Al-Sadeq International Conference on Multidisciplinary in IT and Communication Science and Applications (AIC-MITCSA), pp. 1–6 (2016)Google Scholar
  8. 8.
    Gupta, P., Srivastva, P., Bhardwaj, S., Bhateja, V.: A modified PSNR metric based on HVS for quality assessment of color images. In: International Conference on Communication and Industrial Application, pp. 1–4 (2011)Google Scholar
  9. 9.
    Zhu, S., Zhang, S., Ran, C.: An improved inter-frame prediction algorithm for video coding based on fractal and H.264. In: IEEE Early Access, p. 1 (2017)CrossRefGoogle Scholar
  10. 10.
    Padmashree Rohini, S., Padma, N.: Different approaches for implementation of fractal image compression on medical images. In: IEEE International Conference on Electrical, Electronics, communication and optimization techniques, pp. 66–72 (2016)Google Scholar
  11. 11.
    Padmashree, S., Nagpadma, R.: Comparative analysis of JPEG compression and fractal image compression for medical images. Int. J. Eng. Sci. Technol., 1847–1853 (2013)Google Scholar
  12. 12.
    Rahul, M., Hartenstein, H.: Optimal fractal coding is NP-HARD. In: Proceedings IEEE, Data Compression Conference, pp.. 261–270 (1997)Google Scholar
  13. 13.
    Amin, Q., Ali, N., Ali, A., Nodehi, S.: Square function for population size in quantum evolutionary algorithm and its application in fractal image compression. In Sixth International Conference on Bio-Inspired Computing: Theories and Applications, pp. 3–8 (2011)Google Scholar
  14. 14.
    Yang, Y., Bai, G., Chiribella, G.: Masahito Hayashi: compression for quantum population coding. In IEEE International Symposium on Information Theory(ISIT), pp. 1973–1977 (2017)Google Scholar
  15. 15.
    Songlin, D., Yaping, Y., Yide, M.: Quantum-accelerated fractal image compression-an interdisciplinary approach. IEEE Signal Process. Lett. 22(4) (2015)Google Scholar
  16. 16.
    Hirota, K., Le, P.Q., Lliyasu, A.M., Dong, F.: Strategies for designing geometric transformations on quantum images. Theor. Comput. Sci. 412, 1406–1418 (2011)Google Scholar
  17. 17.
    Yuan, S., Mao, X., Xue, Y., Xiong, Q.: A compare: SQR: a simple quantum representation of infrared images. Quantum Inf. Process. 13(6), 1353–1379 (2014)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Li, H.-S., Qingxin, Z., Lan, S., Shen, C.-Y., Zhou, R., Mo, J.: Image storage, retrival, compression and segmentation in a quantum system. Quantum Inf. Process. 12, 2269–2290 (2013)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Sun, B., Lliyasu, A., Yan, F., Dong, F., Hirota, K.: An RGB multi channel representation for images on quantum computers. J. Adv. Comput. Intell Inform. 17(3), 404–417 (2013)CrossRefGoogle Scholar
  20. 20.
    Caraiman, S., Manta, V.: Image representation and processing using ternary quantum computing. Adapt. Nat. Comput. Algorithms 7824, 366–375 (2013). Springer, BerlinCrossRefGoogle Scholar
  21. 21.
    Lliyasu, A.M.: Towards secure and efficient image and video processing applications on quantum computers. Entropy 15(8), 2874–2974 (2013)MathSciNetCrossRefGoogle Scholar
  22. 22.
    USC-SIPI Image Database. http://sipi.usc.edu/database

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Faculty of EngineeringPacific Academy of Higher Education and Research CenterUdaipurIndia
  2. 2.St. Xavier’s Technological InstituteMumbaiIndia

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