A Fast Progressive Image Transmission Algorithm Using Linear Bivariate Splines

  • Rohit Verma
  • Ravikant Verma
  • P. Syamala Jaya Sree
  • Pradeep Kumar
  • Rajesh Siddavatam
  • S. P. Ghrera
Part of the Communications in Computer and Information Science book series (CCIS, volume 94)


Progressive image transmission provides a convenient User Interface when images are transmitted slowly. In this paper, we present a progressive image reconstruction scheme based on the multi-scale edge representation of images. In the multi-scale edge representation an image is decomposed into Most Significant Points which represent the strong edges and Insignificant Points which represent weak edges. Image re-construction is done based on the approximation of image regarded as a function, by a linear spline over adapted Delaunay triangulation. The proposed method progressively improves the quality of the reconstructed image till the desired quality is obtained.


Delaunay Triangulation Spline Space Strong Edge Linear Spline Weak Edge 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Verma, R., Srivastava, G.K., Mahrishi, R., Siddavatam, R.: A Fast Image Reconstruction Algorithm Using Significant Sample Point Selection and Linear Bivariate Splines. In: IEEE TENCON, pp. 1–6. IEEE Press, Singapore (2009)Google Scholar
  2. 2.
    Verma, R., Srivastava, G.K., Mahrishi, R., Siddavatam, R.: A Novel Wavelet Edge Detection Algorithm For Noisy Images. In: IEEE International Conference on Ultra Modern Technologies, pp. 1–8. IEEE Press, St. Petersburg (2009)Google Scholar
  3. 3.
    Verma, R., Srivastava, G.K., Mahrishi, R., Siddavatam, R.: A Novel Image Reconstruction Using Second Generation Wavelets. In: IEEE International Conference on Advances in Recent Technologies in Communication and Computing, pp. 509–513. IEEE Press, Kerala (2009)CrossRefGoogle Scholar
  4. 4.
    Siddavatam, R., Sandeep, K., Mittal, R.K.: A Fast Progressive Image Sampling Using Lifting Scheme And Non-Uniform B-Splines. In: IEEE International Symposium on Industrial Electronics, pp. 1645–1650. IEEE Press, Spain (2007)Google Scholar
  5. 5.
    Eldar, Y., Lindenbaum, M., Porat, M., Zeevi, Y.Y.: The Farthest Point Strategy For Progressive Image Sampling. IEEE Trans. Image Processing 6(9), 1305–1315 (1997)CrossRefGoogle Scholar
  6. 6.
    Arigovindan, M., Suhling, M., Hunziker, P., Unser, M.: Variational Image Reconstruction From Arbitrarily Spaced Samples: A Fast Multiresolution Spline Solution. IEEE Trans. on Image Processing 14(4), 450–460 (2005)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Vazquez, C., Dubois, E., Konrad, J.: Reconstruction of Nonuniformly Sampled Images in Spline Spaces. IEEE Trans. on Image Processing 14(6), 713–724 (2005)CrossRefGoogle Scholar
  8. 8.
    Cohen, A., Mate, B.: Compact Representation Of Images By Edge Adapted Multiscale Transforms. In: IEEE International Conference on Image Processing, Tessaloniki, pp. 8–11 (2001)Google Scholar
  9. 9.
    Laurent, D., Nira, D., Armin, I.: Image Compression by Linear Splines over Adaptive Triangulations. Signal Processing 86(4), 1604–1616 (2006)zbMATHGoogle Scholar
  10. 10.
    Tzu-Chuen, L., Chin-Chen, C.: A Progressive Image Transmission Technique Using Haar Wavelet Transformation. International Journal of Innovative Computing, Information and Control 3, 6(A), 1449–1461 (2007)Google Scholar
  11. 11.
    Eldar, Y., Oppenheim, A.: Filter Bank Reconstruction of Bandlimited Signals from Non-Uniform and Generalized Samples. IEEE Trans. Signal Processing 48(10), 2864–2875 (2000)CrossRefMathSciNetGoogle Scholar
  12. 12.
    Aldroubi, A., Grochenig, K.: Nonuniform Sampling and Reconstruction in Shift Invariant Spaces. SIAM Rev. 43, 585–620 (2001)zbMATHCrossRefMathSciNetGoogle Scholar
  13. 13.
    Wu, J., Amaratunga, K.: Wavelet Triangulated Irregular Networks. Int. J. Geographical Information Science 17(3), 273–289 (2003)CrossRefGoogle Scholar
  14. 14.
    Barber, C.B., Dobkin, D.P., Huhdanpaa, H.T.: The Quickhull Algorithm for Convex Hulls. ACM Transactions on Mathematical Software 22(4), 469–483 (1996)zbMATHCrossRefMathSciNetGoogle Scholar
  15. 15.
    Preparata, F.P., Shamos, M.I.: Computational Geometry. Springer, New York (1988)Google Scholar
  16. 16.
    Rippa, S.: Minimal Roughness Property of the Delaunay Triangulation. Comput. Aided Geometric Des. 7, 489–497 (1990)zbMATHCrossRefMathSciNetGoogle Scholar
  17. 17.
    Said, A., Pearlman, W.A.: A New, Fast, and Efficient Image Codec Based on Set Parttitioning in Hierarchical Trees. IEEE Trans. on Circuits and Systems for Video Technology 6(3), 243–250 (1996)CrossRefGoogle Scholar
  18. 18.
    Bodson, D., McConnell, K.R., Schaphorst, R.: FAX: Facsimile Technology and Applications Handbook, pp. 195–199 (1992)Google Scholar
  19. 19.
    Paul, C., Bahram, H.: Progressive Robust Image Transmission. In: 6th International Workshop on Systems, Signals and Image Processing. Lancaster University, UK (1999)Google Scholar
  20. 20.
    Neoh, H.S., Hazanchuk, A.: Adaptive Edge Detection for Real-Time Video Processing using FPGAs. Global Signal Processing (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Rohit Verma
    • 1
  • Ravikant Verma
    • 1
  • P. Syamala Jaya Sree
    • 1
  • Pradeep Kumar
    • 2
  • Rajesh Siddavatam
    • 1
  • S. P. Ghrera
    • 1
  1. 1.Department of Computer Science & IT 
  2. 2.Department of Electronics and CommunicationsJaypee University of Information TechnologyWaknaghatIndia

Personalised recommendations