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Medical and Color Image Compression with Fractal Quadtree with Huffman Coding for Different Threshold Values

  • Sandhya Kadam
  • Vijay Rathod
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 810)

Abstract

Fractal Image Compression (FIC) is characterized by long encoding time and high Compression Ratio (CR). Further, as medical images being voluminous, a high CR is required to reduce the storage space. Fractal Image compression adopts affine transforms. In view of this, the present paper aims in providing an implementation of a hybrid approach by combining Quadtree fractal with Huffman coding with different threshold values and a comparative analysis of the different types of input images such as color as well as different modalities of medical images as MRI and X-ray to achieve high CR by still retaining the quality of the image. The implementation is carried out and results are obtained using MATLAB. The performance parameters as encoding time, compression ratio PSNR, and decoding time are compared. The results have shown that with an increase in threshold value, CR increases with a decrease in image quality for color as well as medical images.

Keywords

Fractal Quad-tree decomposition Hybrid methodology Medical image compression Threshold 

References

  1. 1.
    Jacquin, A.E.: Fractal image coding: a review: In: Proceedings of the IEEE, vol. 81, pp. 1451–1465 (1993)CrossRefGoogle Scholar
  2. 2.
    Eman, A., Loay, E.: George: study of fractal color image compression using YUV component, In: Proceedings of the IEEE 36th International Conference on Computer Software and Applications (2012)Google Scholar
  3. 3.
    Sandhya, D.K., Vijay, R.R.: Fractal based image compression techniques: Int. J. Comput. Appl. 178(1), (2017)Google Scholar
  4. 4.
    Sandhya, K., Vijay, R.R.: DCT with quad tree and Huffman coding for color images. Int. J. Comput. Appl. 173(9), (2017)Google Scholar
  5. 5.
    Sandhya, K., Vijay, R.R.: Fractal Coding for Texture, Satellite and Grayscale images to reduce Searching time and Complexity. In: Intelligent Engineering Informatics, p. 695. Springer (2018)Google Scholar
  6. 6.
    Padmashree, S., Nagpadma, R.: Statistical analysis of objective measures using fractal image compression. In: Proceedings of the IEEE International Conference on Signal and Image Processing Applications, pp. 563–568, (2015)Google Scholar
  7. 7.
    Rupa, S., Mohan, V., Venkatraman, Y.: MRI brain image compression using spatial fuzzy clustering technique. In: Proceedings of the IEEE International Conference on Communication and Signal Processing, India, pp. 915–918 (2014)Google Scholar
  8. 8.
    Mahalaxmi, G.V.: Implementation of image compression using fractal image compresssion and neural network for MRI images: In: Proceedings of the IEEE International Conference on Information Science(ICIS), pp. 60–64 (2016)Google Scholar
  9. 9.
    Al-saidi, N.M.G., Aquil, H.A.: Towards enhancing of fractal image compression via block complexity. In: Proceedings of the IEEE Annual Conference on New Trends in Information and Communication Technology Applications (NTICT), pp. 246–251(2017)Google Scholar
  10. 10.
    Suvidhya, K., Priyanka, A., Rohtash, D.: Improved structure similarity in fractal image compression with quad tree. Indian J. Sci. Res. 46–50 (2017)Google Scholar
  11. 11.
    Padmashree, S., Naga Padma, R.: Different approaches for implementation of fractal image compression on medical images: In: Proceedings of the IEEE International Conference on Electrical, Electronics, Communication and Optimization Techniques(ICEECCOT), pp. 66–72 (2016)Google Scholar
  12. 12.
    Yang, Y., Bai, G., Chiribella, G., Hayashi, M.: Compression for quantum population coding. In: IEEE International Symposium on Information Theory (ISIT), pp. 1973–1977 (2017)Google Scholar
  13. 13.
    Abdul, N., Salih, J.: Fractal coding technique based on different block size. In: IEEE Al. Sadeq International Conference on Multidisciplinary in IT and Communication Science and Applications (AIC-MITCSA), pp. 1–6, (2016)Google Scholar
  14. 14.
    Padmashree, S., Nagapadma, R.: Comparative analysis of JPEG compressionand fractal image compression for medical images. Int. J. Eng. Sci. Technol. 5(11), 1847–1853 (2013)Google Scholar
  15. 15.
    Bhavani, S., Thanushkodi, K.G.: Comparison of fractal coding methods for medical image compression. In: IET Image Processing, vol. 7(7), pp. 686–693 (2013)CrossRefGoogle Scholar
  16. 16.
    Veenadevi, S., Ananth, A.G.: Fractal image compression using quad tree decomposition and Huffman coding. Int. J. Signal Image Process. 3(2) 207–212 (2012)Google Scholar
  17. 17.
    Pinki, Rajesh Mehra: Quad tree decomposition based image analysis using intensity difference method 10(7), 1–10 (2016)Google Scholar
  18. 18.
    SIPI image database. https://www.sipi.usc.edu

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Faculty of EngineeringPacific Academy of Higher Education and ResearchUdaipurIndia
  2. 2.Head of DepartmentSt. Xavier’s Technical InstituteMumbaiIndia

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