CFP last date
20 December 2024
Reseach Article

A Compression Technique for Piecewise Smooth Images based on Transform Coding

by Lince Varghese, Shine P. James
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 140 - Number 11
Year of Publication: 2016
Authors: Lince Varghese, Shine P. James
10.5120/ijca2016909486

Lince Varghese, Shine P. James . A Compression Technique for Piecewise Smooth Images based on Transform Coding. International Journal of Computer Applications. 140, 11 ( April 2016), 9-13. DOI=10.5120/ijca2016909486

@article{ 10.5120/ijca2016909486,
author = { Lince Varghese, Shine P. James },
title = { A Compression Technique for Piecewise Smooth Images based on Transform Coding },
journal = { International Journal of Computer Applications },
issue_date = { April 2016 },
volume = { 140 },
number = { 11 },
month = { April },
year = { 2016 },
issn = { 0975-8887 },
pages = { 9-13 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume140/number11/24637-2016909486/ },
doi = { 10.5120/ijca2016909486 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:42:00.734789+05:30
%A Lince Varghese
%A Shine P. James
%T A Compression Technique for Piecewise Smooth Images based on Transform Coding
%J International Journal of Computer Applications
%@ 0975-8887
%V 140
%N 11
%P 9-13
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Image compression has a wide range of application since it leads to reduction in storage space and easy transmission. Piecewise smooth image consists of sharp edge boundaries and smooth interior surfaces. This paper deals with compression of Piecewise smooth images using Graph Fourier Transform and Discrete Cosine Transform. In order to obtain better quality of reconstructed image blocks contains edge boundaries are transformed using DCT and smooth regions are transformed using both weighted GFT and unweighted GFT. In order to reduce the computational complexity, low pass filter and down sample a high resolution pixel block to obtain a low resolution one at the encoder, so that LR-GFT can be employed. At the decoder upsampling and interpolation are performed so that sharp edge boundaries can be preserved.

References
  1. Wei hu, Gene Cheung, Antonio Ortega, Oscar C. Au “Multiresolution Graph Fourier Transform for compression of piecewise smooth images”, IEEE transactions on image processing, vol. 24, no. 1, January 2015
  2. D. K. Hammond, P. Vandergheynst, and R. Gribonval, “Wavelets on graphs via spectral graph theory,” Appl. Comput. Harmon. Anal., vol. 30, no. 2, pp. 129–150, Mar. 2010.
  3. T. Wiegand, G. J. Sullivan, G. Bjontegaard, and A. Luthra, “Overview of the H.264/AVC video coding standard,” IEEE Trans. Circuits Syst. Video Technol., vol. 13, no. 7, pp. 560–576, Jul. 2003.
  4. J. Huang and P. M. Schultheiss, “Block quantization of correlated Gaussian random variables,” IEEE Trans. Commun. Syst., vol. 11, no. 3, pp. 289–296, Sep. 1963.
  5. V. K. Goyal, J. Zhuang, and M. Vetterli, “Transform coding with backward adaptive updates,” IEEE Trans. Inf. Theory, vol. 46, no. 4,pp. 1623–1633, Jul. 2000.
  6. S. Jana and P. Moulin, “Optimality of KLT for high-rate transform coding of Gaussian vector-scale mixtures: Application to reconstruction, estimation, and classification,” IEEE Trans. Inf. Theory, vol. 52, no. 9, pp. 4049–4067, Sep. 2006.
  7. M. Effros, H. Feng, and K. Zeger, “Suboptimality of the Karhunen–Loeve transform for transform coding,” IEEE Trans. Inf. Theory, vol. 50, no. 8, pp. 1605–1619, Aug. 2004.
  8. A. K. Jain, Fundamentals of Digital Image Processing. Upper Saddle River, NJ, USA: Prentice-Hall, 1989.
  9. H. S. Malvar, A. Hallapuro, M. Karczewicz, and L. Kerofsky, “Low-complexity transform and quantization in H.264/AVC,” IEEE Trans. Circuits Syst. Video Technol., vol. 13, no. 7, pp. 598–603, Jul. 2003.
  10. V. K. Goyal, “Theoretical foundations of transform coding,” IEEE Signal Process. Mag., vol. 18, no. 5, pp. 9–21, Sep. 2001.
  11. P. Wan, Y. Feng, G. Cheung, I. V. Bajic, O. C. Au, and Y. Ji, “3D motion in visual saliency modeling,” in Proc. IEEE Int. Conf. Acoust., Speech Signal Process. (ICASSP), Vancouver, BC, Canada, May 2013, pp. 1831–1835.
  12. D. Tian, P.-L. Lai, P. Lopez, and C. Gomila, “View synthesis techniques for 3D video,” Proc. SPIE, vol. 7443, p. 74430T, Feb. 2009.
  13. S.-C. Han and C. I. Podilchuk, “Video compression with dense motion fields,” IEEE Trans. Image Process., vol. 10, no. 11, pp. 1605–1612, Nov. 2001.
  14. D. S. Hochbaum, “An efficient and effective tool for image segmentation, total variations and regularization,” in Proc. 3rd Int. Conf. Scale Space Variational Methods Comput. Vis., vol. 6667, 2011, pp. 338–349.
  15. J. Shi and J. Malik, “Normalized cuts and image segmentation,”IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 8, pp. 888–905, Aug. 2000.
  16. R. Shukla, P. L. Dragotti, M. N. Do, and M. Vetterli, “Rate-distortion optimized tree-structured compression algorithms for piecewise polynomial images,” IEEE Trans. Image Process., vol. 14, no. 3, pp. 343–359, Mar. 2005.
  17. Y. Morvan, P. H. N. de With, and D. Farin, “Platelet-based coding of depth maps for the transmission of multiview images,” Proc. SPIE, vol. 6055, Jan. 2006, p. 60550K.
  18. P. Merkle et al., “The effects of multiview depth video compression on multiview rendering,” Signal Process., Image Commun., vol. 24, nos. 1–2, pp. 73–88, Jan. 2009.
  19. I. Daribo, G. Cheung, and D. Florencio, “Arithmetic edge coding for arbitrarily shaped sub-block motion prediction in depth video compression,” in Proc. 19th IEEE Int. Conf. Image Process., Orlando, FL, USA, Sep. 2012, pp. 1541–1544.
  20. I. Daribo, D. Florencio, and G. Cheung, “Arbitrarily shaped motion prediction for depth video compression using arithmetic edge coding,” IEEE Trans. Image Process., vol. 23, no. 11, pp. 4696–4708, Nov. 2014.
  21. M. B. Wakin, J. K. Romberg, H. Choi, and R. G. Baraniuk, “Wavelet-domain approximation and compression of 2002.
Index Terms

Computer Science
Information Sciences

Keywords

Image compression Graph Fourier Transform Discrete Cosine Transform Piecewise smooth images