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Biorthogonal Wavelet-based Image Compression

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Artificial Intelligence and Evolutionary Computations in Engineering Systems

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 668))

Abstract

The image compression is required to reduce the size and transmission bandwidth. The wavelet transform-based image compression is more preferable than other techniques such as DCT. The biorthogonal wavelets are more preferable than orthogonal wavelets due to symmetry property and flexibility. This paper proposes image compression using biorthogonal wavelets. The various biorthogonal wavelets are applied to image compression. The bior1.3 wavelet has the highest PSNR and lowest computation time. The bior1.3 wavelet is superior wavelet out of all the biorthogonal wavelets for image compression.

Please note that the AISC Editorial assumes that all authors have used the western naming convention, with given names preceding surnames. This determines the structure of the names in the running heads and the author index.

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References

  1. S. Jayaraman Esakkirajan, T. Veerakumar, Digital Image Processing (Tata Mc Graw Hill Publication, 2009)

    Google Scholar 

  2. R.C. Gonzalez, R.E. Woods, Digital Image processing, 2nd edn. (Prentice Hall of India Ltd, 2004)

    Google Scholar 

  3. R.C. Gonzalez, R.E. Woods, S.L. Eddins, Digital Image processing using MATLAB, 2nd edn. (Prentice Hall of India 2003)

    Google Scholar 

  4. K.H. Talukder, K. dan Harada, Haar Wavelet Based Approach for Image compression and Quality Assessment of Compressed Image, IAENG Int. J. Appl. Mat. 36(1), (2007)

    Google Scholar 

  5. A.K. Jain, Fundamental of Digital Image Processing, 4th edn. (Prentice Hall of India Private Ltd, 2000)

    Google Scholar 

  6. M. Sonka, V. Hlavac, R. Boyle, Image Processing, Analysis, and Machine Vision, 2nd edn. (Vikas Publishing House, 2001)

    Google Scholar 

  7. W.K. Pratt, Digital Image Processing, 3rd edn. (John Wiley & Sons Inc, 2001)

    Google Scholar 

  8. P.M.K. Prasad, D.Y.V. Prasad, G.S. Bhushana Rao, Performance Analysis of orthogonal and Biorthogonal wavelets for Edge detection of X-ray Images, Elsevier Procedia Computer science ISSN: 1877-0509, Vol. 87, 2016, pp. 116–121

    Google Scholar 

  9. K.P. Soman, K.I. Ramachandran, Insight into Wavelets from Theory to Practice, 2nd edn. (Prentice Hall of India, 2008)

    Google Scholar 

  10. L. Feng, C.Y. Suen, Y.Y. Tang, L.H. Tang, Edge extraction of images by reconstruction using wavelet decomposition details at different resolution levels. Int. J. Pattern Recognit. Artif. Intell. 14(6), 779–793, (2000)

    Google Scholar 

  11. P.J. Vanfleet, Discrete Wavelet Transformations an Elementary Approach with Applications. (Wiley, 2011)

    Google Scholar 

  12. R.C. Gonzalez, R.E. Woods, Digital Image Processing, (Pearson Education, 2004)

    Google Scholar 

  13. F.Y. Cui, L.J. Zou, B. Song, Edge feature extraction based on digital image processing techniques in Proceedings of the IEEE, International Conference on Automation and Logistics, Qingdao, China September 2008, pp. 2320–2324

    Google Scholar 

  14. C.S. Burrus, R.A. Gopinath, Haitiao Guo, Introduction to Wavelets and Wavelets Transforms: A Primer. (China Machine Press, Beijing, 2005)

    Google Scholar 

  15. P. Singh, P. Singh, R.K. Sharma, JPEG image compression based on biorthogonal, coiflets and daubechies wavelet families. Int. J. Comp. Appl. (0975–8887). 13(1), (2011)

    Google Scholar 

  16. D. Gnanadurai, V. Sadasivam, An efficient adaptive thresholding technique for wavelet based image denoising. Int. J. Electron. Commun. Engg. 2(8), (2008)

    Google Scholar 

  17. S.G. Chang, B. Yu, M. Vetterli, Adaptive wavelet thresholding for image denoising and compression. IEEE Trans. Image Process 9(9), (2000)

    Google Scholar 

  18. A. McAndrew, Introduction to Digital Image Processing with MATLAB (Cengage Learning India Private Limited, New Delhi, 2009)

    Google Scholar 

  19. S. Mallat, A Wavelet Tour of Signal Processing (Academic Press, USA, 1999)

    MATH  Google Scholar 

  20. M. Antonini, M. Barland, P. Mathien, I. Daubechies, Image coding using wavelet transform. IEEE Trans. Image Process. 1, 205–220 (1992)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, G.V.P. College of Engineering for Women, Visakhapatnam, India

    P. M. K. Prasad

  2. Department of Electronics and Communication Engineering, AIET, Viziangaram, India

    G. Umamadhuri

Authors
  1. P. M. K. Prasad
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  2. G. Umamadhuri
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Corresponding author

Correspondence to P. M. K. Prasad .

Editor information

Editors and Affiliations

  1. Electrical and Electronics Engineering, SRM Engineering College, Kattankulathur, Tamil Nadu, India

    Subhransu Sekhar Dash

  2. Department of Electrical and Electronics Engineering, Madanapalle Institute of Technology and Science, Madanapalle, Andhra Pradesh, India

    Paruchuri Chandra Babu Naidu

  3. Department of Electrical and Electronics Engineering, Gazi University, Ankara, Turkey

    Ramazan Bayindir

  4. Indian Statistical Institute, Kolkata, India

    Swagatam Das

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Prasad, P.M.K., Umamadhuri, G. (2018). Biorthogonal Wavelet-based Image Compression. In: Dash, S., Naidu, P., Bayindir, R., Das, S. (eds) Artificial Intelligence and Evolutionary Computations in Engineering Systems. Advances in Intelligent Systems and Computing, vol 668. Springer, Singapore. https://doi.org/10.1007/978-981-10-7868-2_38

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  • DOI: https://doi.org/10.1007/978-981-10-7868-2_38

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-7867-5

  • Online ISBN: 978-981-10-7868-2

  • eBook Packages: EngineeringEngineering (R0)

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