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Image Compression Using Edge-Enhancing Diffusion

  • Lipsa Behera
  • Bibhuprasad Mohanty
  • Madhusmita Sahoo
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 309)

Abstract

Image compression technique minimizes the size in bytes of a graphics file without degrading the quality of the image to an unacceptable visual level. This piece of work deals with the method of image compression by preserving the edge information intact as the human visual system is much sensitive to these information. This is done by the use of Perona–Malik method for diffusion where the whole image is smoothened but the edge. Terming this as edge-enhancing diffusion (EED), we apply two established coding techniques, namely singular value decomposition (SVD) and set partitioning in hierarchical trees (SPHIT). Even though the above encoding schemes enjoy more superiority and advantages in their respective domain, extensive simulation for the proposed diffusion platform provides still better results in terms of PSNR and visual quality.

Keywords

Perona–Malick diffusion Partial differential equations Image compression Nonlinear diffusion SVD SPHIT 

References

  1. 1.
    Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. In: Proceedings of IEEE Computer Society Workshop on Computer Vision, pp. 16–22 (Nov 1987)Google Scholar
  2. 2.
    Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12(7), 629–639 (1990)CrossRefGoogle Scholar
  3. 3.
    Sapiro, G.: From active contours to anisotropic diffusion: relations between basic PDEs in image processing. In: Proceedings ICIP, Lausanne, Switzerland (Sep 1996)Google Scholar
  4. 4.
    Shah, J.: A common framework for curve evolution, segmentation, and anisotropic diffusion. In: Proc CVPR, San Francisco, CA, pp. 136–142 (June 1996)Google Scholar
  5. 5.
    Barash, D., Comaniciu, D.: A common framework for nonlinear diffusion, adaptive smoothing, bilateral filtering and mean shift. Image Vis. Comput. 22(1), 73–81 (2004)CrossRefGoogle Scholar
  6. 6.
    Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. PAMI-8, 679–698 (1986)CrossRefGoogle Scholar
  7. 7.
    Black, M.J., Sapiro, G., Marimont, D.H., Heeger, D.: Robust anisotropic diffusion. IEEE Trans. Image Process. 7(3), 421–432 (1998)CrossRefGoogle Scholar
  8. 8.
    Linear Algebra and its Application, 3rd edn. In: Lay, D.C. Addison-Wesley Publishing Co., Boston (2002)Google Scholar
  9. 9.
    Numerical Analysis. In: Saucer, T. George Mason University, Pearson Education Inc, Pearson (2006)Google Scholar
  10. 10.
    An investigation into using SVD as a method of image compression, http://www.haroldthecat.f2s.com/project
  11. 11.
    Said, A., Pearlman, W.A.: A new, fast and efficient image codec based on set partitioning in hierarchical trees. IEEE Trans. Circ. Syst. Video Technol. 6, 243–250 (1996)CrossRefGoogle Scholar
  12. 12.
    Kim, B.-J., Xiong, Z., Pearlman, W.A.: Low bit-rate scalable video coding with 3-D set partitioning in hierarchical trees (3-D SPIHT). IEEE Trans. Circ. Syst. Video Technol. 10(8), 1374–1387 (2000)CrossRefGoogle Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  • Lipsa Behera
    • 1
  • Bibhuprasad Mohanty
    • 1
  • Madhusmita Sahoo
    • 1
  1. 1.Department of ECE, ITER (Faculty of Engineering)Siksha O Anusandhana UniversityBhubaneswarIndia

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