Call for Paper - January 2022 Edition
IJCA solicits original research papers for the January 2022 Edition. Last date of manuscript submission is December 20, 2021. Read More

A Modified Context based Image Interpolation Algorithm for Digital Images

Print
PDF
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Sandeepa K. S., B. N. Jagadale, J. S. Bhat
10.5120/ijca2017914990

Sandeepa K S., B N Jagadale and J S Bhat. A Modified Context based Image Interpolation Algorithm for Digital Images. International Journal of Computer Applications 171(2):34-37, August 2017. BibTeX

@article{10.5120/ijca2017914990,
	author = {Sandeepa K. S. and B. N. Jagadale and J. S. Bhat},
	title = {A Modified Context based Image Interpolation Algorithm for Digital Images},
	journal = {International Journal of Computer Applications},
	issue_date = {August 2017},
	volume = {171},
	number = {2},
	month = {Aug},
	year = {2017},
	issn = {0975-8887},
	pages = {34-37},
	numpages = {4},
	url = {http://www.ijcaonline.org/archives/volume171/number2/28157-2017914990},
	doi = {10.5120/ijca2017914990},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

A modified context based interpolation algorithm, for digital images, is presented. In the proposed algorithm, the unknown pixel value is exploited based upon the characteristic of a neighboring pixel by considering its edge. The edge is obtained by taking differences of two slopes calculated from neighboring pixels, which are in orthogonal direction. The algorithm uses fourth ordered prediction based approach when interpolating new pixel value by giving suitable weights to the neighboring pixels. This method gives better results as compared to some of existing interpolation methods. Comparison has done using Peak Signal to Noise Ratio (PSNR) and Correlation Coefficient method.

References

  1. E. Maeland, “On the comparison of interpolation methods” IEEE Transactions on Medical Imaging, vol. 7, no. 3, September, 1988.
  2. Hou, and H. Andrews, “Cubic splines for image interpolation and digital filtering” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 26, no. 6, pp. 508-517,1978.
  3. R. Keys, “Cubic convolution interpolation for digital image processing” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 29, no. 6, pp. 1153-1160, 1981
  4. Xin Li and Michael T. Orchard “New Edge-Directed Interpolation, in IEEE Transaction On Image Processing, Vol. 10, No. 10, October 2001.
  5. Xiangjun Zhang and Xiaolin Wu “Image Interpolation by Adaptive 2- D Autoregressive Modeling and Soft-Decision Estimation,” in IEEE Transaction On Image Processing, Vol. 17, No. 6, June 2008.
  6. Vinit Jakhetiya and Anil K. Tiwari, “Image interpolation by adaptive 2 -D autoregressive modeling,” in International Conference on Digital Image Processing, 2010.
  7. Tai-Wai Chan; Au, O.C.; Tak-Song Chong; Wing-San Chau; “A novel content-adaptive interpolation,” in IEEE International Symposium on Circuits and Systems, 2005. , vol., no., pp. 6260- 6263 Vol. 6.
  8. Jakhetiya, V.; Jaiswal, S.P.; Tiwari, A.K. “A computationally efficient context based switched image interpolation algorithm for natural images, in I2MTC, 2011 IEEE , vol., no., pp.1-4, 10-12 May 2011.
  9. Dataset of Standard 512x512 Grayscale Test Images. Universidad de Granada: http://decsai.ugr.es/cvg/CG/base.htm
  10. Sunil Prasad Jaiswal, yVinit Jakhetiya, _Ayush Kumar, zAnil Kumar Tiwari “A Low Complex Context Adaptive Image Interpolation Algorithm for Real-Time Applications” in IEEE international conference 2012.

Keywords

Prediction coefficient, interpolation, low resolution image, neighboring pixels, slope identification, weight estimator.