International Journal of Emerging Research in Engineering, Science, and Management
Vol. 1, Issue 1, pp. 05-10, Jan-Mar 2022.
https://doi.org/10.58482/ijeresm.v1i1.2

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Design and Implementation of New Biorthogonal Wavelets and its Application to Image Processing

E Sasikala Reddy

TVV Sathyanarayana

Assistant Professor, Dept. of ECE, Gokula Krishna College of Engineering, Sullurpet

Associate Professor, Dept. of ECE, Sri Vidyanikethan Engineering College, Tirupati

Abstract: Wavelet transformation has been an interesting field since its exposure with wavelet-based compression standard embedded zerotree wavelet. Though, the origin of wavelets back to many decades, the presence of research at the very beginning of wavelet bases is also being carried out in the research community. This is because of the wide presence of its applicability as well as its structure of adapting to the type of problem at hand. In this paper, an approach of designing biorthogonal wavelets is presented. This approach may be extended to design variety of wavelets. This paper mainly focus on the design steps and corresponding coding in MATLAB. By varying the length, type of the bases and other parameters, different wavelets may be devised.

Keywords: Biorthogonal wavelets, Spline function, wavelet base, wavelet design.

References
  1. R. A. DeVore, “Adaptive Wavelet Bases for Image Compression,” in Wavelets, Images and Surface Fitting, pp. 197-219, 1994.
  2. G. Strintzis, “Optimal Biorthogonal Wavelet Bases for Signal Decomposition,” IEEE Transactions on Signal Processing, vol. 44, no. 6, pp. 1406-1417, Jun. 1996.
  3. G. Kharate, V. Patil, and N. Bhale, “Selection of Mother Wavelet for Image Compression on Basis of Image,” Journal of Multimedia, vol. 2, no. 6, Nov. 2007.
  4. V. S. Chourasia and A. K. Tiwari, “Design Methodology of a New Wavelet Basis Function for Fetal Phonocardiographic Signals,” The Scientific World Journal, vol. 2013, Article ID 505840, 2013. https://doi.org/10.1155/2013/505840
  5. N. K. Al-Qazzaz, S. H. Bin Mohd Ali, S. Anom Ahmad, M. Shabiul Islam, and J. Escudero, “Selection of Mother Wavelet Functions for Multi-Channel EEG Signal Analysis during a Working Memory Task,” Sensors, vol. 15, pp. 29015-29035, 2015.
  6. G. Garg, “A Signal Invariant Wavelet Function Selection Algorithm,” Medical & Biological Engineering & Computing, vol. 54, pp. 629-642, 2016.
  7. J. M. Shapiro, “Embedded Image Coding Using Zerotrees of Wavelet Coefficients,” IEEE Transactions on Signal Processing, vol. 41, no. 12, pp. 3445-3462, 1993.
  8. A. Said and W. A. Pearlman, “A New, Fast, and Efficient Image Codec Based on Set Partitioning in Hierarchical Trees,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 6, no. 3, pp. 243-250, Jun. 1996.
  9. J. K. Sunkara, E. Navaneethasagari, D. Pradeep, E. Naga Chaithanya, D. Pavani, and D. V. Sai Sudheer, “A New Video Compression Method Using DCT/DWT and SPIHT Based on Accordion Representation,” International Journal of Image, Graphics and Signal Processing, pp. 28-34, May 2012.
  10. J. K. Sunkara, P. Kuruma, and R. Sankaraiah, “Image Compression Using Hand Designed and Lifting Based Wavelet Transforms,” International Journal of Electronics Communications and Computer Technology, vol. 2, no. 4, 2012.
  11. J. K. Sunkara, K. Purnima, E. Navaneetha Sagari, and L. Rama Subbareddy, “A New Accordion Based Video Compression Method,” i-manager's Journal on Electronics Engineering, vol. 1, no. 4, pp. 14-21, Jun.-Aug. 2011.
  12. J. K. Sunkara, K. Purnima, S. Muchakala, and Y. Ravisankaraiah, “Super-Resolution Based Image Reconstruction,” International Journal of Computer Science and Technology, vol. 2, no. 3, pp. 272-281, Sep. 2011.
  13. R. Sankaraiah and S. Varadarajan, “Autoregressive Models and Non-Local Self Similarity in Sparse Representation for Image Deblurring,” Journal of Advanced Research in Dynamical and Control Systems, vol. 10, no. 11, 2018.
  14. T. Gopi and T. Sudha, “A Novel Image Watermarking System Utilizing HMM in Wavelet Domain,” International Journal of Computer Science Engineering and Information Technology Research, vol. 6, no. 2, pp. 1-10, Apr. 2016.
  15. G. A. E. Satish Kumar and J. K. Sunkara, “Multiresolution SVD Based Image Fusion,” IOSR Journal of VLSI and Signal Processing, vol. 7, no. 1, pp. 20-27, Jan.-Feb. 2017. https://doi.org/10.9790/4200-0701012027
  16. J. K. Sunkara, S. Eswarawaka, K. Darisi, S. Dara, P. K. Dasari, and P. Dara, “Intensity Non-uniformity Correction for Image Segmentation,” IOSR Journal of VLSI and Signal Processing, vol. 1, no. 5, pp. 49-57, Jan.-Feb. 2013.
  17. J. K. Sunkara, U. K. Panta, N. Pemmasani, C. S. Paricherla, P. Pattasani, and V. Pattem, “Region Based Active Contour Model for Intensity Non-uniformity Correction for Image Segmentation,” International Journal of Engineering Research and Technology, vol. 6, no. 1, pp. 61-73, 2013.
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