Skip to main content
Log in

Sequency-based mapped real transform: properties and applications

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Transforms are tools that decompose a signal into alternate representation. Fourier transform is very popular for frequency domain analysis, and the computations are performed in the complex domain. Mapped real transform (MRT), whose basis functions are rectangular in nature, was evolved by modification of discrete Fourier transform and involves real additions only. But it is expansive and redundant. Unique MRT was developed to remove the redundancies present in MRT. Visual representation of unique MRT coefficients was explored to establish a transform, according to sequency, entitled sequency-based MRT (SMRT). It is a sequency ordered, addition oriented, integer-to-integer transform, whose basis functions are orthogonal. This paper gives an insight into the visual representation, transform kernel and placement of the \(N^2\) unique MRT coefficients corresponding to an \(N \times N\) data, for N a power of 2. Different properties of SMRT, computation of statistical parameters from transform coefficients are also included. Scope of the proposed transform in pattern generation is also investigated in addition to the established applications.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Majumdar, A.: Image compression by sparse PCA coding in curvelet domain. Signal Image Video Process. 3(1), 27–34 (2008)

    Article  MATH  Google Scholar 

  2. Asmare, M.H., Asirvadam, V.S., Hani, A.F.M.: Image enhancement based on contourlet transform. Signal Image Video Process. 9(7), 1679–1690 (2014)

    Article  Google Scholar 

  3. Lajevardi, S.M., Hussain, Z.M.: Automatic facial expression recognition: feature extraction and selection. Signal Image Video Process. 6(1), 159–169 (2010)

    Article  Google Scholar 

  4. Swartzlander, E.E.: Systolic FFT processors: a personal perspective. J. Signal Process. Syst. 3(1), 3–14 (2007)

    Google Scholar 

  5. Gopikakumari, R.: Investigations on the development of an ANN model and visual manipulation approach for 2D DFT computation in image processing. Ph.D. Dissertation, CUSAT, Kochi (1998)

  6. Roy, R.C., Gopikakumari, R.: A new transform for 2-D signal representation (MRT) and some of its properties. In: IEEE Int. Conf. on Signal Processing and Communications, pp. 363–367 (2004)

  7. Roy, R.C., Kumar, M.S.A., Gopikakumari, R.: An invertible transform for image representation and its application to image compression. In: Proc. of the Int. Symposium on Signal Processing and Its Applications, pp. 1–4 (2007)

  8. Roy, R.C.: Development of a new transform MRT. Ph.D. Dissertation, CUSAT, Kochi (2009)

  9. Bhadran, V.: Development and implementation of visual approach and parallel distributed architecture for 2D-DFT and UMRT computation. Ph.D. Dissertation, CUSAT, Kochi (2009)

  10. Basu, P., Bhadran, V., Gopikakumari, R.: A new algorithm to compute forward and inverse 2-D UMRT for \(N\) a power of 2. In: 2nd Int. Conf. on Power, Signals, Control and Computation, Trichur (2012)

  11. Bhadran, V., Roy, R.C., Gopikakumari, R.: Visual representation of 2-D DFT in terms of \(2\times 2 \)data, a pattern analysis. In: Proc. of Int. Conf. on Computing, Communication and Networking, Karur (2008)

  12. Jaya, V.L., Basu, P., Gopikakumari, R.: SMRT: A new placement approach of 2-D unique MRT coefficients for \(N\) a power of 2. In: Annual IEEE India Conference, pp. 233–237, Kochi (2012)

  13. Meenakshy, K.: Development and implementation of a CAD system to predict the fragmentation of renal stones based on texture analysis of CT images. Ph.D. Dissertation, CUSAT, Kochi (2010)

  14. Manju, B., Jaya, V.L., Meenakshy, K., Gopikakumari, R.: \(8\times 8\) SMRT based texture descriptors. In: Lecture Notes on Software Engineering, vol. 3, pp. 295–298 (2015)

  15. Kumar, M.S.A., Basu, P., Gopikakumari, R.: UMRT based adaptive block size transform coder for images using quad-tree partitioning. Int. J. Sci. Eng. Res. 4(12), 1170–1176 (2013)

  16. Kumar, M.S.A.: Development of 2-D MRT based image compression techniques. Ph.D. Dissertation, CUSAT, Kochi (2013)

  17. Jaya, V.L., Gopikakumari, R.: Automatic enhancement of low contrast images using SMRT. Int. J. Sci. Eng. Res. 4(9), 1510–1515 (2013)

  18. Jaya, V.L., Gopikakumari, R.: Fuzzy intensification operator based image enhancement in the SMRT domain. In: Int. Conf. on Signal and Speech Proc., pp. 214–220 (2014)

  19. Jaya, V.L., Gopikakumari, R.; Fuzzy rule based enhancement in the SMRT domain for low contrast images. Int. Conf. on Information and Communication Technologies (2014)

  20. Roy, R.C., Gopikakumari, R.: Relationship between the Haar Transform and the MRT. In: Proc. of the 8th International Conference on Information, Communication and Signal Processing, pp. 1–5 (2011)

Download references

Acknowledgements

The authors would like to thank Rajesh Cherian Roy and Bhadran V for their contributions in the development of MRT and UMRT which gave the base for the development of SMRT.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to V. L. Jaya.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jaya, V.L., Gopikakumari, R. Sequency-based mapped real transform: properties and applications. SIViP 11, 1551–1558 (2017). https://doi.org/10.1007/s11760-017-1119-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-017-1119-2

Keywords

Navigation