Skip to main content

Introduction

  • Chapter
  • First Online:
Lossy Image Compression

Part of the book series: SpringerBriefs in Computer Science ((BRIEFSCOMPUTER))

  • 814 Accesses

Abstract

This chapter brings the subject matter into perspective and presents a historical review of image compression in moderate detail.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Pratt WK (1978) Digital image processing. Wiley, New York, pp 591–710

    Google Scholar 

  2. Pratt WK (1979) Image transmission techniques. Academic press, New York

    Google Scholar 

  3. AN Netravali, JO Limb (1980) Picture coding: a review. In: Proceeings of IEEE, vol 68. pp 366–406, Mar 1980

    Google Scholar 

  4. Special issue on bandwidth communication (1977) IEEE Transactions on communications, Nov 1977

    Google Scholar 

  5. Sayood K (1996) Introduction to data compression. Margan Kaufman, San Francisco

    MATH  Google Scholar 

  6. Nelson M, Gailly JL (1996) The data compression book, 2nd edn. M&T Publishing Inc, New York

    Google Scholar 

  7. Distasi R, Nappi M, Vitulano S (1997) Image compression by B-tree triangular coding. IEEE Trans Commun 45(9):1095–1100

    Article  Google Scholar 

  8. Digital compression and coding of continuous-tone still images, part I, requirements and guidelines (1991) ISO/IEC JTC1 Committee draft 10918-1, Feb 1991

    Google Scholar 

  9. Digital compression and coding of continuous-tone still images, part II, Compliance draft (1991) ISO/IEC JTC1 Committee draft 10918-2

    Google Scholar 

  10. Wallace GK (1991) The JPEG still picture compression standard. Commun ACM 34:30–44

    Article  Google Scholar 

  11. Gregory W, Delp J (1993) The use of high performance computing in JPEG image compression. In: Twenty seventh conference on signal, systems, and computers, Pacific Grove, California

    Google Scholar 

  12. Cook GW, Delp J (1994) An investigation of JPEG image and video compression using parallel processing. In: Proceedings of ICASSP, pp 437–440

    Google Scholar 

  13. Cook GW, Delp DJ (1996) An investigation of scalable SIMD I/O techniques with application to parallel JPEG compression. J Parallel Distrib Comput 53:111–128

    Google Scholar 

  14. Jiang J, Grecos C (2001) A low cost design of rate controlled JPEG-LS near lossless image compression. Image Vis Comput 19:153–164

    Article  Google Scholar 

  15. Boliek M, Christopoulos C, Majani E (2000) JPEG2000 part I final draft international standard. (ISO/IEC FDIS15444-1), ISO/IES JTC/SC29/WG1n1855, Aug 2000

    Google Scholar 

  16. The JPEG2000 still image compression standard—ISO/IEC/JTC 1/SC 29/WG 1, Report Sep 2001

    Google Scholar 

  17. Christopoulos C, Skodras A, Ebrahimi T (2000) The JPEG2000 still image coding system: an overview. IEEE Trans Consum Electron 46(4):1103–1127

    Article  Google Scholar 

  18. Skodras AN, Christopoulos CA, Ebrahimi T (2001) JPEG2000: the upcoming still image compression standard. Pattern Recognit Lett 22:1337–1345

    Article  MATH  Google Scholar 

  19. Taubman D, Ordentlich E, Weinberger M, Seroussi G (2002) Embedded block coding in JPEG2000. Signal Process Image Commun 17:49–72

    Article  Google Scholar 

  20. Askelof J, Carlander M, Christopoulos C (2002) Region of interest coding in JPEG2000. Signal Process Image Commun 17:105–111

    Article  Google Scholar 

  21. AK Jain ( 1981) Image data compression: a review. In: Proceedings of the IEEE, vol 69, p 3

    Google Scholar 

  22. Linde Y, Buzo A, Gray RM (1980) An algorithm for vector quantizer design. IEEE Trans Commun 28(1):84–94

    Article  Google Scholar 

  23. Rosenfeld A (1984) The diameter of fuzzy set. Fuzzy Sets Syst 13:241–246

    Article  MathSciNet  MATH  Google Scholar 

  24. Rosenfeld A, Haber S (1985) The perimeter of fuzzy set. Pattern Recognit 18:125–130

    Article  MathSciNet  MATH  Google Scholar 

  25. Pal SK, Rosenfeld A (1988) Image enhancement and thresholding by optimization of fuzzy compactness. Pattern Recognit Lett 7:77–86

    Article  MATH  Google Scholar 

  26. Pal SK, Ghosh A (1990) Index of area coverage of fuzzy image subsets and extraction. Pattern Recognit Lett 831–841

    Google Scholar 

  27. Pal SK, Ghosh A (1992) Fuzzy geometry in image analysis. Fuzzy Sets Syst 48(1):22–40

    Article  MathSciNet  Google Scholar 

  28. Pal NR, Pal S (1993) A review on image segmentation techniques. Pattern Recognit 26:1277–1294

    Article  Google Scholar 

  29. Kaleva O (1994) Interpolation of fuzzy data. Fuzzy Sets Syst 61:63–70

    Article  MathSciNet  MATH  Google Scholar 

  30. Eskicioglu AM, Fisher PS (1995) Image quality measures and their performance. IEEE Trans Commun 43(12):2959–2965

    Article  Google Scholar 

  31. Karayiannis NB, Pai P-I (1995) Fuzzy vector quantization algorithms and their applications in image compression. IEEE Trans Image process 4(9):1193–1201

    Article  Google Scholar 

  32. Wu Xiaolin, Fang Yonggang (1995) A segmentation-based predictive multiresolution image coder. IEEE Trans Image Process 4:34–47

    Article  Google Scholar 

  33. Bramble JH, Zlamal M (1970) Triangular elements in the finite elements method. Math Comput 24(112):809–820

    Article  MathSciNet  Google Scholar 

  34. Babuska I, Aziz AK (1976) On the angle condition in the finite element method. Siam J Numer Anal 13(2):214–226

    Article  MathSciNet  MATH  Google Scholar 

  35. Rippa S (1992) Long and thin triangles can be good for linear interpolation. Siam J Numer Anal 29(1):257–270

    Article  MathSciNet  MATH  Google Scholar 

  36. Aurenhammer F, Katoh N, Kojima H, Ohsaki M, Xu Y (2002) Approximating uniform triangular meshes in polygons. Theor Comput Sci 289:879–895

    Article  MathSciNet  MATH  Google Scholar 

  37. Diwan AA, Kurhekar MP (2002) Plane triangulations are 6-partitionable. Discrete Math 256:91–103

    Article  MathSciNet  MATH  Google Scholar 

  38. Mitra SK, Murthy CA, Kundu MK (2000) A technique for image magnification using partitioned iterative function system. Pattern Recognit 33:1119–1133

    Article  Google Scholar 

  39. Eckert MP, Bradley AP (1998) Perceptual quality metrics applied to still image compression. Signal Process 70:177–200

    Article  MATH  Google Scholar 

  40. Sadesh I (1996) Polynomial approximation of images. Comput Math Appl 32(5):99–115

    Article  MathSciNet  Google Scholar 

  41. Prasad MVNK, Mishra VN, Shukla KK (2003) Space partitioning based image compression using quality measures. Appl Soft Comput Elsevier Sci 3:273–282

    Article  Google Scholar 

  42. Biswas S (2003) Segmentation based compression for gray level images. Pattern Recognit 36:1501–1517

    Article  Google Scholar 

  43. Cheng HD, Li J (2003) Fuzzy homogeneity and scale space approach to color image segmentation. Pattern Recognit 36:1545–1562

    Article  Google Scholar 

  44. J Redford (2003) Parallelizing JPEG. In: ISPC proceedings, Dallas, March 21–April 3

    Google Scholar 

  45. Prasad MVNK, Mishra VN, Shukla KK (2002) Implementation of BTTC image compression algorithm on parallel virtual machine. J Comput Soc India 32(3):1–8, ISSN 0254-7813

    Google Scholar 

  46. Guibas L, Stolfi J (1985) Primitives for the manipulation of general subdivisions and the computation of voronoi diagrams. ACM Trans Graph 4(2):74–123

    Article  MATH  Google Scholar 

  47. Plaza A, Rivara M-C (2002) On the adjacencies of triangular meshes based on skeleton-regular partitions. J Comput Appl Math 140:673–693

    Article  MathSciNet  MATH  Google Scholar 

  48. Boissonnant JD, Cazals F (2002) Smooth surface reconstruction via natural neighbour interpolation of distance functions. Comput Geom 22:185–203

    Article  MathSciNet  Google Scholar 

  49. Aurenhammer F (1991) Voronoi diagrams—a survey of a fundamental geometric data structure. ACM Comput Surv 23(3):345–405

    Article  Google Scholar 

  50. Lundmark A, Wadstromer N, Li H (2001) Hierarchical subsampling giving fractal regions. IEEE Trans Image Process 4(1):167–173

    Article  Google Scholar 

  51. Vreelj B, Vaidyanathan PP (2001) Efficient implementation of all digital interpolation. IEEE Trans Image Process 10(11):1639–1646

    Article  Google Scholar 

  52. Fan J, Yau DKY, Elmagarmid AK, Araf G (2001) Automatic image segmentation by integrating color edge extraction on image processing. IEEE Trans Image Process 10(10):1454–1466

    Article  MATH  Google Scholar 

  53. Kotlov A (2001) Note: tree width and regular triangulations. Discrete Math 237:187–191

    Article  MathSciNet  MATH  Google Scholar 

  54. Miguet S, Pierson JM (2000) Quality and complexity bounds of load balancing algorithms for parallel image processing. Int J Pattern Recognit Artif Intell 14(4):463–476

    Article  Google Scholar 

  55. Sibeyn JF (2000) Solving fundamental problems on sparse-meshes. IEEE Trans Parallel Distrib Syst 11(12):1324–1332

    Article  Google Scholar 

  56. Rosenfeld A (2000) Survey image analysis and computer vision: 1999. Comput Vis Image Underst 78:222–302

    Article  Google Scholar 

  57. Weinberger MJ, Seroussi G, Sapiro G (2000) The LOCO–I lossless image compression algorithm: principles and standardization into JPEG-LS. IEEE Trans Image Process 9(8):1309–1324

    Article  Google Scholar 

  58. Taubman D (2000) High performance scalable image compression with EBCOT. IEEE Trans Image Process 9(7):1158–1170

    Article  Google Scholar 

  59. de Queiroz RL (2000) Optimizing block-thresholding segmentation for multilayer compression of compound images. IEEE Trans Image Process 9(9):1461–1471

    Article  MathSciNet  MATH  Google Scholar 

  60. wang L, He L, Mutoh A, Nakamura T, Itoh H (1999) Fuzzy reasoning for image compression using adaptive triangular plane patches. Fuzzy Sets Syst 113:277–284

    Article  Google Scholar 

  61. Toivanen PJ, Vepsalainen AM, Parkkinen JPS (1999) Image compression using the distance transform on curved space (DTOCS) and Delaunay triangulation. Pattern Recognit Lett 20:1015–1026

    Article  Google Scholar 

  62. Miyahara M, Kotani K, Algazi VR (1998) Objective picture quality scale (PQS) for image coding. IEEE Trans Image Process 46(5):1215–1225

    Google Scholar 

  63. Rosenfield A (1998) Fuzzy geometry: an updated overview. Inf Sci 110:123–133

    Google Scholar 

  64. Mitchell SA (1997) Approximating the maximum-angle covering triangulation. Comput Geom 7:93–111

    Article  MathSciNet  MATH  Google Scholar 

  65. Radha H, Vetterli M, Leonardi R (1996) Image compression using binary space partitioning trees. IEEE Trans Image Process 5(12):1610–1623

    Article  Google Scholar 

  66. Davoine F, Antonini M, Chassery J-M, Barlaud M (1996) Fractal image compression based on delaunay triangulation and vector quantization. IEEE Trans Image Process 5(2):338–346

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to K. K. Shukla .

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag London Limited

About this chapter

Cite this chapter

Shukla, K.K., Prasad, M.V. (2011). Introduction. In: Lossy Image Compression. SpringerBriefs in Computer Science. Springer, London. https://doi.org/10.1007/978-1-4471-2218-0_1

Download citation

  • DOI: https://doi.org/10.1007/978-1-4471-2218-0_1

  • Published:

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-2217-3

  • Online ISBN: 978-1-4471-2218-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics