Image Compression Using Quality Measures

  • K. K. ShuklaEmail author
  • M. V. Prasad
Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)


This chapter discusses domain decomposition algorithms using quality measures like average difference, entropy, mean squared error and a fuzzy geometry measure called fuzzy compactness. All the partitioning methods discussed in this chapter execute in O(nlogn) time for encoding and θ(n) time for decoding, where n is the number of pixels in the image.


Image quality Average difference Entropy ME Fuzzy sets Fuzzy compactness 


  1. 1.
    Wu X, Fang Y (1995) A segmentation-based predictive multiresolution image coder. IEEE Trans Image Process 4:34–47CrossRefGoogle Scholar
  2. 2.
    Strobach P (1989) Image coding based on quadtree-structured recursive least squares approximation. Proc IEEE ICAASP 89:1961–1964Google Scholar
  3. 3.
    Wu X (1992) Image coding by adaptive tree-structured segmentation. IEEE Trans Inform Theory 38(6):1755–1767MathSciNetzbMATHCrossRefGoogle Scholar
  4. 4.
    Jiang W, Bruton L (1998) Lossless color image compression using chromatic correlation. In: IEEE proceedings of data compression conferenceGoogle Scholar
  5. 5.
    Da Silva VC, De Carvalho JM (2000) Image compression via TRITREE decomposition. In: Proceedings of the XIII Brazilian symposium on computer graphics and image processing (SIBGRAPI)Google Scholar
  6. 6.
    Li X, Knipe J, Cheng H (1997) Image compression and encryption using tree structures. Pattern Recognit Lett 18:1253–1259CrossRefGoogle Scholar
  7. 7.
    Vitulano S, Di Ruberto C, Nappi M (1997) Different methods to segment biomedical images. Pattern Recognit Lett 18:1125–1131CrossRefGoogle Scholar
  8. 8.
    Lundmark A, Wadstromer N, Li H (2001) Hierarchical subsampling giving fractal regions. IEEE Trans Image Process 4(1):167–173CrossRefGoogle Scholar
  9. 9.
    Biswas S, Pal NR (2000) On hierarchical segmentation for image compression. Pattern Recognit Lett 21:131–144CrossRefGoogle Scholar
  10. 10.
    Eckert MP, Bradley AP (1998) Perceptual quality metrics applied to still image compression. Signal Process 70:177–200zbMATHCrossRefGoogle Scholar
  11. 11.
    Davoine F, Antonini M, Chassery JM, Barlaud M (1996) Fractal image compression based on delaunay triangulation and vector quantization. IEEE Trans Image Process 5(2):338–346CrossRefGoogle Scholar
  12. 12.
    Eskicioglu AM, Fisher PS (1995) Image quality measures and their performance. IEEE Trans Commun 43(12):2959–2965CrossRefGoogle Scholar
  13. 13.
    Rosenfeld A (1984) The fuzzy geometry of image subsets. Pattern Recognit Lett 2:311–317CrossRefGoogle Scholar
  14. 14.
    Pal SK, Rosenfeld A (1988) Image enhancement and thresholding by optimization of fuzzy compactness. Pattern Recognit Lett 7:77–86zbMATHCrossRefGoogle Scholar
  15. 15.
    Pal SK (1989) Fuzzy skeletanization of an image. Pattern Recognit Lett 10:17–23zbMATHCrossRefGoogle Scholar
  16. 16.
    Rosenfeld A (1979) Fuzzy digital topology. Inform Control 40:76–87MathSciNetzbMATHCrossRefGoogle Scholar
  17. 17.
    Rosenfeld A (1983) On connectivity properties of gray scale pictures. Pattern Recognit 16:47–50CrossRefGoogle Scholar
  18. 18.
    Hirota K, Pedryez W (2002) Data compression with fuzzy relational equations. Fuzzy Sets Syst 126:325–335zbMATHCrossRefGoogle Scholar
  19. 19.
    Tizhoosh HR, Krell G, Michaelis G (1997) On fuzzy enhancement of megavoltage images in radiation therapy. In: IEEE conference on fuzzy systems, FUZZ-IEEE’97, vol 3. Barcelona, Spain, pp 1399–1404Google Scholar
  20. 20.
    Tizhoosh HR, Michaelis B (1999) Subjectivity, psychology and fuzzy techniques: a new approach to image enhancement. In: Proceedings of 18th international conference of NAFIPS’99, New York, pp 522–526Google Scholar
  21. 21.
    Nobuhara H, Pedrycz W, Harota K (2000) Fast solving method of fuzzy relational equation and its application to lossy image compression/reconstruction. IEEE Trans Fuzzy Sys 8(3): 325–334CrossRefGoogle Scholar
  22. 22.
    Sinha D, Sinha P, Dougherty ER, Batman S (1997) Design and analysis of fuzzy morphological algorithms for image processing. IEEE Trans Fuzzy Syst 5:570–584CrossRefGoogle Scholar
  23. 23.
    Prasad MVNK, Mishra VN, Shukla KK (2003) Space partitioning based image compression using quality measures. Applied soft computing, vol. 3. Elsevier Science, Amsterdam, pp. 273–282Google Scholar
  24. 24.
    Prasad MVNK, Shukla KK, Mukherjee RN (2002) Implementation of BTTC image compression Algorithm Using Fuzzy Technique, AFSS2002. In: Proceedings of the international conference on fuzzy systems, Calcutta, pp 375–381, February 2002, ISBN 3-540-43150-0Google Scholar
  25. 25.
    Distasi R, Nappi M, Vitulano S (1997) Image compression by B-tree triangular coding. IEEE Trans Commun 45(9):1095–1100CrossRefGoogle Scholar
  26. 26.
    Jain AK (1981) Image data compression: a review. Proc IEEE 69(3):349–389CrossRefGoogle Scholar
  27. 27.
    Pal SK, Ghosh A (1990) Index of area coverage of fuzzy image subsets and extraction. Pattern Recognit Lett 11:831–841zbMATHCrossRefGoogle Scholar
  28. 28.
    Pal SankarK, Ghosh Ashish (1992) Fuzzy geometry in image analysis. Fuzzy Sets Syst 48:22–40MathSciNetCrossRefGoogle Scholar
  29. 29.
    Rosenfeld A, Haber S (1985) The perimeter of fuzzy set. Pattern Recognit 18:125–130MathSciNetzbMATHCrossRefGoogle Scholar
  30. 30.
    Rosenfeld A (1984) The diameter of fuzzy set. Fuzzy Sets Systems 13:241–246MathSciNetzbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited  2011

Authors and Affiliations

  1. 1.Department of Computer EngineeringIndian Institute of Technology, Banaras Hindu UniversityVaranasiIndia
  2. 2.Institute for Development and Research in Banking TechnologyHyderabadIndia

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