Academia.eduAcademia.edu

Outline

Title
Abstract
Introduction
Conclusions
References
All Topics
Computer Science
Algorithms and Data Structures

Visual data compression for multimedia applications

Profile image of Murat KUNTMurat KUNT

1998, Proceedings of the IEEE

https://doi.org/10.1109/5.687832
visibility

…

description

17 pages

link

1 file

Abstract

The compression of visual information in the framework of multimedia applications is discussed. To this end, major approaches to compress still as well as moving pictures are reviewed. The most important objective in any compression algorithm is that of compression efficiency. High-compression coding of still pictures can be split into three categories: waveform, second-generation, and fractal coding techniques. Each coding approach introduces a different artifact at the target bit rates. The primary objective of most ongoing ...

Related papers

Survey paper on image compression techniques
sudha rawat

2017

This paper attempts to give a best approach for selecting one of the popular image compression algorithms based on (a) Wavelet, (b) JPEG/DCT, (c) VQ, and (d) Fractal approaches. We review and discuss the advantages and disadvantages of these algorithms for compressing grayscale images and colored images. Here we are trying to find the best performance approach amongst the several compression algorithms. This paper shows that all of the four approaches perform satisfactorily when the 0.5 bits per pixel (bpp) is desired. However, for a low bit rate compression like 0.25 bpp or lower, the embedded zerotree wavelet (EZW) approach, SPHIT approach and DCT-based JPEG approach are more practical.

View PDFchevron_right
A hybrid image compression algorithm based on fractal coding and wavelet transform
Thanos Stouraitis

2000

Recently, in order to achieve satisfactory image and video quality and fast transmission in sometimes very low bandwidth channels, the demand for high compression ratios and fast speed in the coding and decoding procedure has been increased. A novel algorithm for very high compression of images is proposed in this paper. First of all the image is decomposed through a

View PDFchevron_right
Analysis of Image Compression Methods Based On Transform and Fractal Coding
Arindam Mondal

This is to certify that the thesis entitled, -Analysis of Image Compression Methods Based On Transform and Fractal Coding" submitted by Archana Deshlahra to

View PDFchevron_right
Subject Review : Various Techniques for Image Compression
International Journal of Scientific Research in Science, Engineering and Technology IJSRSET

International Journal of Scientific Research in Science, Engineering and Technology, 2023

Due to the quick development of multimedia technology, now can see the increasing amount of multimedia data that is being generated or transmitted over the internet. digital image is one form of multimedia data, that can take different formats and different sizes. And here comes the main objective of image compression in transmitting or storing the data in an effective manner by decreasing the redundant or irrelevant information without significant loss of visual quality in image data. Compression of the images is utilized in various services, including TV broadcasts, satellite imagery, martial communications, webinars, medical imaging, weather reporting, etc. This paper provides a survey of the main techniques in image compression, covering both lossy & lossless approaches. The techniques that depend on lossy compression lose some of the image details during compression, while lossless compression techniques keep the image information without losing. Vector and scalar Quantization, transform coding, block truncation Coding, etc. are all examples of lossy approaches. Run length coding, entropy encoding, statistical coding, etc. are all examples of lossless techniques. This paper will assist the researchers in learning about these techniques and choosing appropriate techniques for their work.

View PDFchevron_right
A Review of Image Compression and Comparison of its Algorithms
deepthi aruna

Image compression is now essential for applications such as transmission and storage in data bases. In this paper we review and discuss about the image compression, need of compression, its principles, and classes of compression and various algorithm of image compression. This paper attempts to give a recipe for selecting one of the popular image compression algorithms based on Wavelet, JPEG/DCT, VQ, and Fractal approaches. We review and discuss the advantages and disadvantages of these algorithms for compressing grayscale images, give an experimental comparison on 256×256 commonly used image of Lenna and one 400×400 fingerprint image.

View PDFchevron_right
A Comparative study on Image and Video Compression Techniques
IOSR Journals

In the present era of Internet multimedia data especially Images and Videos are the most widely used digital format for data transmission. However due to their large data sizes and constraint of low bandwidth capacity of communication channel it is very difficult to transmit them at optimum speed maintaining the signal quality. Compression therefore, is a vital tool that not only reduces the data size thereby leading to faster data transmission but also protects it to some extent from transmission errors. A large variety of Image and Videos compression techniques are employed each having their own strengths and weaknesses. This paper is an effort to present an overview of image and video compression techniques, their working and comparison.

View PDFchevron_right
A simplified fractal image compression algorithm
Mohiy Hadhoud

2008 International Conference on Computer Engineering & Systems, 2008

This paper proposes a simplified fractal image compression algorithm which is implemented on a block by block basis. This algorithm achieves a compression ratio of up to 10 with a peak signal to noise ratio (PSNR) as high as 35dB. The idea of the proposed algorithm is based on the segmentation of the image, first, into blocks to setup reference blocks. The image is then decomposed again into block ranges and a search process is carried out to find the reference blocks with best match. The transmitted or stored values, after compression, are the reference block values and the indices of the reference block that achieves the best match. If there is no match, the average value of the block range is transmitted or stored instead. The advantages of the proposed algorithm are the simplicity of computation and the high PSNR achieved.

View PDFchevron_right
Image Compression: A Survey
Mudassar Raza

Image Compression is a demanding field in this era of communication. There is a need to study and analyze the literature for image compression, as the demand for images, video sequences and computer animation has increased at very high rate so that the increment is drastically over the years. Multimedia data whether graphics, audio, video data which is uncompress requires considerable transmission bandwidth and storage capacity. So this leads to the need of compression of images and all multimedia applications to save storage and transmission time. In this study we discuss different compression algorithms used to reduce size of images without quality reduction.

View PDFchevron_right
Comparative Study Of Techniques For Image Compression
Sachin Prasad

2017

Now a days image storage and transmission has become one of the biggest concerns due to shortage of storage and delay in transmission. Therefore image compression is very much needed for faster transmission and saving of space. The existing technologies have a number of limitations like loss of information, lower compression ratios, lower PSNR and high compression time. Fractal image compression using quad tree decomposition and Huffman coding has been designed and used on a set of standard images like Lena, cameraman and baboon. The results obtained yield good compression ratio and PSNR. The method is also applied to set of fractal images and satellite images. The results obtained are found to be better for fractals and satellite images compared to other existing methods. https://journalnx.com/journal-article/20150410

View PDFchevron_right
Review on Image Compression Techniques
International Journal of Scientific Research in Science, Engineering and Technology IJSRSET

Image compression is the type of data compression on digital images. The main objective is to reduce redundancy of the image. In image compression, we do not only concentrate on reducing size but also concentrate on doing it without losing quality and information of image. The survey summarizes the major image compression techniques that maybe lossy and lossless, advantages, disadvantages and research possibilities. This paper attempts to give a review of run length coding, Huffman coding, DCT, fractal and Talbot effects. We discuss advantages and disadvantages of these algorithms.

View PDFchevron_right

References (155)

  1. "Digital compression and coding of continuous-tone still im- ages," ISO/IEC JTC1 Committee Draft, JPEG 8-R8, 1990.
  2. Joint Photographic Experts Group. JPEG home page. [Online].
  3. Available WWW: http://www.disc.org.uk/ public/jpeghomepage.htm
  4. W. B. Pennebacker and J. L. Mitchell, JPEG Still Image Compression Standard. New York: Van Nostrand, 1993.
  5. L. Chiariglione, "MPEG and multimedia communications," IEEE Trans. Circuits Syst. Video Technol., vol. 7, pp. 5-18, Feb. 1997.
  6. "Information technology-Coding of moving pictures and associated audio for digital storage media up to about 1.5
  7. Mbit/s-Part 2: Coding of moving pictures information," ISO/IEC JTC1 CD 11172, MPEG, 1991.
  8. D. LeGall, "MPEG: A video compression standard for multi- media applications," Commun. ACM, vol. 34, no. 4, pp. 46-58, Apr. 1991.
  9. "Information technology-Generic coding of moving pictures and associated audio information-Part 2: Video," ISO/IEC DIS 13818-2, MPEG-2, 1994.
  10. Digital Audio Visual Council. What is DAVIC? [Online]. Avail- able WWW: http://www.davic.org/WHATIS.htm.
  11. DVD frequently asked questions. [Online]. Available WWW: http://www.videodiscovery.com/vdyweb/dvd/dvdfaq.html.
  12. DVB-Television for the third millennium. [Online]. Available WWW: http://www.dvb.org/.
  13. "Information technology-Coding of audio-visual object: Vi- sual," ISO/IEC JTC1 CD 14496-2 (MPEG-4), Oct. 1997.
  14. R. Koenen, F. Pereira, and L. Chiariglione, "MPEG-4: Context and objectives," Signal Process., vol. 9, no. 4, pp. 295-304, May 1997.
  15. M. Liou, "Overview of the p 64 Kbit/s video coding standard," Commun. ACM, vol. 34, no. 4, pp. 60-63, Apr. 1991.
  16. "Recommendation H.261-Video codec for audiovisual ser- vices at p 64 Kbit/s," CCITT SG XV, COM XV-R37-E, 1990.
  17. "Video coding for low bitrate communication," ITU-T SG XV, DRAFT H.263, 1996.
  18. "Video coding for low bitrate communication," ITU-T SG XVI, DRAFT 13 H.263+ Q15-A-60 rev. 0, 1997.
  19. Moving Picture Experts Group. MPEG home page. [Online]. Available: http://drogo.cselt.stet.it/mpeg/.
  20. "New work item: JPEG 2000 image coding system," ISO/IEC JTC1/SC29WG1 N390R, JPEG2000, 1997.
  21. "Call for contributions for JPEG 2000 image coding system," ISO/IEC JTC1/SC29WG1 N505, JPEG2000, 1997.
  22. D. E. Pearson and J. A. Robinson, "Visual communications at very low data rates," Proc. IEEE, vol. 73, pp. 795-812, Apr. 1985.
  23. T. Ebrahimi, E. Reusens, and W. Li, "New trends in very low bitrate video coding," Proc. IEEE, vol. 83, pp. 877-891, June 1995.
  24. N. Ahmed, T. Natarajan, and K. R. Rao, "Discrete cosine transform," IEEE Trans. Comput., vol. C-23, pp. 90-93, Jan. 1974.
  25. P. J. Burt and E. H. Adelson, "The Laplacian pyramid as a compact image code," IEEE Trans. Commun., vol. COM-31, pp. 532-540, Apr. 1983.
  26. A. Croisier, D. Esteban, and C. Galand, "Perfect channel splitting by use of interpolation, decimation, tree decomposition techniques," in Proc. Int. Conf. Information Sciences/Systems, Aug. 1976. pp. 443-446.
  27. M. J. T. Smith and T. P. Barnwell, "Exact reconstruction techniques for tree structured subband coders," IEEE Trans. Acoust., Speech, Signal Processing, vol. ASSP-34, pp. 434-441, June 1986.
  28. M. Vetterli, "Multi-dimensional subband coding: Some theory and algorithms," Signal Process., vol. 6, pp. 97-112, Feb. 1984.
  29. J. Woods and S. O'Neil, "Subband coding of images," IEEE Trans. Acoust., Speech, Signal Processing, vol. ASSP-34, pp. 1278-1288, Oct. 1986.
  30. J. D. Johnston, "A filter family designed for use in quadrature mirror filter banks," in Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP), Apr. 1980, pp. 291-294.
  31. E. H. Adelson, E. Simoncelli, and R. Hingorani, "Orthogonal pyramid transforms for image coding," in Proc. SPIE Conf. Visual Communications and Image Processing (VCIP), Oct. 1987, vol. 845, pp. 50-58.
  32. H. Gharavi and A. Tabatabai, "Subband coding of monochrome and color images," IEEE Trans. Circuits Syst., vol. 35, pp. 207-214, Feb. 1988.
  33. J. W. Woods, Subband Image Coding. Boston, MA: Kluwer, 1991.
  34. A. Nicoulin, M. Mattavelli, W. Li, and M. Kunt, "Subband image coding using jointly localized filter banks and entropy coding based on vector quantization," Opt. Eng., vol. 32, no. 7, pp. 1438-1450, July 1993.
  35. C. K. Chui, An Introduction to Wavelets. New York: Aca- demic, 1992.
  36. G. Strang and T. Nguyen, Wavelets and Filter Banks. Cam- bridge, MA: Wellesley-Cambridge Press, 1996.
  37. K. H. Goh, J. Soraghan, and T. S. Durrani, "Multi-resolution based algorithms for low bit-rate image coding," in Proc. IEEE Int. Conf. Image Processing (ICIP), Austin, TX, Nov. 1994, vol. II, pp. 285-289.
  38. J. M. Shapiro, "An embedded wavelet hierarchical image coder," in Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP), San Francisco, CA, Mar. 1992, vol. IV, pp. 657-660.
  39. "Application of the embedded wavelet hierarchical image coder to very low bit rate image coding," in Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP), Minneapolis, MN, Apr. 1993, vol. V, pp. 558-561.
  40. "Embedded image coding using zerotrees of wavelet coefficients," IEEE Trans. Signal Processing, vol. 41, pp. 3445-3462, Dec. 1993.
  41. I. Moccagatta and M. Kunt, "A pyramidal vector quantization approach to transform domain," in Proc. European Signal Processing Conf. (EUSIPCO), Brussels, Belgium, Aug. 1992, vol. III, pp. 1365-1368.
  42. "VQ and cross-band prediction for color image coding," in Proc. Picture Coding Symp. (PCS), Sacramento, CA, Sept. 1994, pp. 383-386.
  43. O. Egger and W. Li, "Subband coding of images using asym- metrical filter banks," IEEE Trans. Image Processing, vol. 4, pp. 478-485, Apr. 1995.
  44. O. Egger, A. Nicoulin, and W. Li, "Embedded zerotree based image coding using linear and morphological filter banks," in Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP), Detroit, MI, May 1995.
  45. H. Caglar, Y. Liu, and A. N. Akansu, "Optimal PR-QMF design for subband image coding," J. Visual Commun. Image Representation, vol. 4, no. 4, pp. 242-253, Sept. 1993.
  46. A. N. Akansu, R. A. Haddad, and H. Caglar, "The binomial QMF-Wavelet transform for multiresolution signal decompo- sition," IEEE Trans. Signal Processing, vol. 41, pp. 13-19, Jan. 1993.
  47. O. Egger and W. Li, "Very low bit rate image coding using morphological operators and adaptive decompositions," in Proc. IEEE Int. Conf. Image Processing (ICIP), Austin, TX, Nov. 1994, vol. II, pp. 326-330.
  48. O. Egger, W. Li, and M. Kunt, "High compression image cod- ing using an adaptive morphological subband decomposition," Proc. IEEE, vol. 83, pp. 272-287, Feb. 1995.
  49. D. A. F. Florencio and R. W. Schafer, "A nonexpansive pyramidal morphological image coder," in Proc. IEEE Int. Conf. Image Processing (ICIP), Austin, TX, Nov. 1994, vol. II, pp. 331-335.
  50. M. Kunt, A. Ikonomopoulos, and M. Kocher, "Second- generation image coding techniques," Proc. IEEE, vol. 73, pp. 549-574, Apr. 1985.
  51. M. Kunt, M. Benard, and R. Leonardi, "Recent results in high- compression image coding," IEEE Trans. Circuits Syst., vol. CAS-34, pp. 1306-1336, Nov. 1987.
  52. A. Ikonomopoulos and M. Kunt, "High compression image coding via directional filtering," Signal Process., vol. 8, no. 2, pp. 179-203, Apr. 1985.
  53. M. Kocher and M. Kunt, "Image data compression by contour texture modeling," in Proc. SPIE Int. Conf. Applications of Digital Image Processing, Geneva, Switzerland, Apr. 1983, pp. 131-139.
  54. S. Carlsson, "Sketch-based coding of grey level images," Signal Process., vol. 15, no. 1, pp. 57-83, July 1988.
  55. X. Ran and N. Farvardin, "Adaptive DCT image coding on a three-component image model," in Proc. IEEE Int. Conf. Acous- tics, Speech and Signal Processing (ICASSP), San Francisco, CA, Mar. 1992, vol. III, pp. 201-204.
  56. "Low bit-rate image coding using a three-component image model," University of Maryland, College Park, Tech. Rep. TR 92-75, 1992.
  57. S. G. Mallat and S. Zhong, "Characterization of signals from multiscale edges," IEEE Trans. Pattern Anal. Machine Intell., vol. 14, pp. 710-732, July 1992.
  58. J. Froment and S. G. Mallat, "Second generation compact image coding with wavelets," in Wavelets-A Tutorial in Theory and Applications, C. Chui, Ed. New York: Academic, Jan. 1992.
  59. M. F. Barnsley, Fractals Everywhere. San Diego, CA: Aca- demic, 1988.
  60. A. E. Jacquin, "Image coding based on a fractal theory of iterated contractive image transformation," IEEE Trans. Image Processing, vol. 1, pp. 18-30, Jan. 1992.
  61. Y. Fischer, "A discussion of fractal image compression," in Chaos and Fractals, H. O. Peitgen, H. Jurgens, and D. Saupe, Eds. New York: Springer-Verlag, 1992.
  62. G. E. Oien, "L2-optimal attractor image coding with fast de- coder convergence," Ph.D. dissertation, Institutt for Teleteknikk, Universitetet I Trondheim, Trondheim, Norway, 1993.
  63. S. Ono, N. Ohta, and T. Aoyama, Super-High-Definition Im- ages: Beyond HDTV. Norwood, MA: Artech House, 1995.
  64. K. Barthel, T. Voy'e, and P. Noll, "Improved fractal im- age coding," in Proc. Picture Coding Symp. (PCS), Lausanne, Switzerland, Mar. 1993, no. 1.5.
  65. E. W. Jacobs, Y. Fisher, and R. D. Boss, "Image compression: A study of iterated transform method," Signal Process., vol. 29, pp. 251-263, Dec. 1992.
  66. A. Baskurt and R. Goutte, "3-dimensional image compres- sion by discrete cosine transform," in Proc. European Signal Processing Conf. (EUSIPCO), 1988, vol. IV, pp. 79-82.
  67. G. Karlsson and M. Vetterli, "Three dimensional subband cod- ing of video," in Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP), Apr. 1988, vol. II, pp. 1100-1103.
  68. T. Ebrahimi and M. Kunt, "Image sequence coding using a three dimensional wavelet packet and adaptive selection," in Proc. SPIE Conf. Visual Communications and Image Processing (VCIP), Boston, MA, Nov. 1992, vol. 1818, pp. 222-232.
  69. E. Chang and A. Zakhor, "Scalable video coding using 3- D subband velocity coding and multirate quantization," in Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP), Apr. 1993, vol. V, pp. 574-577.
  70. H. G. Musmann, P. Pirsh, and H. Grallert, "Advances in picture coding," Proc. IEEE, vol. 73, pp. 523-548, Apr. 1985.
  71. F. Dufaux and F. Moscheni, "New perspectives in motion estimation techniques for digital TV," Proc. IEEE, vol. 83, pp. 858-876, June 1995.
  72. J. R. Ohm, "Temporal domain subband video coding with motion compensation," in Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP), San Francisco, CA, Mar. 1992, vol. III, pp. 229-232.
  73. "Three dimensional SBC-VQ with motion compensa- tion," in Proc. Picture Coding Symposium (PCS), Lausanne, Switzerland, Mar. 1993, no. 11.5.
  74. "Three-dimensional subband coding with motion com- pensation," IEEE Trans. Image Processing, vol. 3, pp. 559-571, Sept. 1994.
  75. M. Gilge, "A high quality videophone coder using hierarchi- cal motion estimation and structure coding of the prediction error," in Proc. SPIE Conf. Visual Communications and Image Processing (VCIP), Nov. 1988, vol. 1001, pp. 864-874.
  76. C. Stiller and D. Lappe, "Laplacian pyramid coding of predic- tion error images," in Proc. SPIE Conf. Visual Communications and Image Processing (VCIP), Nov. 1991, vol. 1605, pp. 47-57.
  77. W. E. Lynch and A. Reibman, "The lapped orthogonal trans- form (LOT) for motion-compensated video compression," in Proc. SPIE Conf. Visual Communications and Image Processing (VCIP), Nov. 1991, vol. 1605, pp. 285-296.
  78. Y.-Q. Zhang and S. Zafar, "Motion-compensated wavelet trans- form coding for color video compression," in Proc. SPIE Conf. Visual Communications and Image Processing (VCIP), Nov. 1991, vol. 1605, pp. 301-316.
  79. W. Li and F. Mateo, "Segmentation based coding of motion compensated prediction error images," in Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP), Minneapo- lis, MN, Apr. 1993, vol. V, pp. 357-360.
  80. W. Li and M. Kunt, "Morphological segmentation applied to displaced frame difference coding," Signal Process., vol. 38, no. 1, pp. 45-56, July 1994.
  81. K. N. Ngan and W. L. Chooi, "Very low bit rate video coding using 3D subband approach," IEEE Trans. Circuits Syst., vol. 4, pp. 309-316, June 1994.
  82. C. Podilchuk, "Low bit-rate subband video coding," in Proc. IEEE Int. Conf. Image Processing (ICIP), Austin, TX, Nov. 1994, vol. III, pp. 280-284.
  83. P. H. Westerink, J. Biemond, and F. Muller, "Subband coding of image sequences at low bit rates," Signal Process., vol. 2, no. 4, pp. 441-448, Dec. 1990.
  84. T. Ebrahimi and M. Kunt, "A video codec based on perceptually derived and localized wavelet transform for mobile applica- tions," in Proc. European Signal Processing Conf. (EUSIPCO), 1992, pp. 1361-1364.
  85. D. Thoreau and J.-F. Vial, "Very low bit rate subband coding," in Proc. Picture Coding Symposium (PCS), Sacramento, CA, Sept. 1994, vol. 11-10, pp. 304-307.
  86. D. Qian, "Block-based motion compensated subband coding at very low bit rates using a psychovisual model," in Proc. Very Low Bitrate Video Workshop (VLBV), Essex, U.K., 1994, no. 6.8.
  87. K. Dachiku et al., "Motion compensation subband ex- tra/interpolative prediction coding at very low bit rate," in Proc. Very Low Bitrate Video Workshop (VLBV), Essex, U.K., 1994, no. 6.2.
  88. J. Katto, J. Ohki, S. Nogaki, and M. Ohta, "A wavelet codec with overlapped motion compensation for very low bit-rate environment," IEEE Trans. Circuits Syst. Video Technol., vol. 4, pp. 328-338, June 1994.
  89. P. Willemin, T. R. Reed, and M. Kunt, "Image sequence coding at very low bit rates with a 3-D split and merge algorithm," in Proc. 2nd Int. Workshop 64 Kb/s coding of Moving Video, Hannover, Germany, Sept. 1989.
  90. "Image sequence coding by split and merge," IEEE Trans. Commun., vol. 39, pp. 1845-1855, Dec. 1991.
  91. P. Salembier and M. Pardas, "Hierarchical morphological seg- mentation for image sequence coding," IEEE Trans. Image Processing, vol. 3, pp. 639-651, Sept. 1994.
  92. P. Salembier, L. Torres, F. Meyer, and C. Gu, "Region-based coding using mathematical morphology," Proc. IEEE, vol. 83, pp. 843-857, June 1995.
  93. F. Bossen and T. Ebrahimi, "A simple and efficient binary shape coding technique based on bitmap representation," in Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing (ICASSP).
  94. M. Hotter, "Object-oriented analysis-synthesis coding based on moving two-dimensional objects," Signal Process., vol. 2, no. 4, pp. 409-428, Dec. 1990.
  95. H. Jozawa and H. Watanabe, "Video coding using segment- based affine motion compensation," in Proc. Picture Coding Symposium (PCS), Sacramento, CA, Sept. 1994, pp. 238-241.
  96. A. Rosenfeld and A. C. Kak, Digital Picture Processing, vol. 2. New York: Academic, 1982.
  97. H. Freeman, "On the encoding of arbitrary geometric configura- tions," IRE Trans. Electron. Comput., vol. EC-10, pp. 260-268, June 1961.
  98. C. R. Dyer, A. Rosenfeld, and H. Samet, "Region representa- tion: Boundary codes from quadtrees," Commun. ACM, vol. 23, no. 3, pp. 171-179, Mar. 1980.
  99. H. Samet, "Region representation: Quadtrees from boundary codes," Commun. ACM, vol. 23, no. 3, pp. 163-170, Mar. 1980.
  100. H. Blum and R. N. Nagel, "Shape description using weighted symmetric axis features," Pattern Recognition, vol. 3, pp. 167-180, 1978.
  101. P. Brigger, S. Ayer, and M. Kunt, "Morphological shape rep- resentation of segmented images based on temporally modeled motion vectors," in Proc. IEEE Int. Conf. Image Processing (ICIP), Nov. 1994, vol. III, pp. 756-760.
  102. C. Gu and M. Kunt, "Contour simplifications and motion compensation for very low bit-rate video coding," in Proc. IEEE Int. Conf. Image Processing (ICIP), Nov. 1994, vol. II, pp. 423-427.
  103. Y. Yokoyama, Y. Miyamoto, and M. Ohta, "Very low bit-rate video coding with object-based motion compensation and oth- ogonal transform," in Proc. SPIE Conf. Visual Communications and Image Processing (VCIP), Nov. 1993, vol. 2049, pp. 12-23.
  104. C. Gu and M. Kunt, "Contour image sequence coding by motion compensation and morphological filters," in Proc. Very Low Bitrate Video Workshop (VLBV), Essex, U.K., 1994, no. 7.1.
  105. J. R. Casas and L. Torres, "Coding of details in very low bit- rate video systems," IEEE Trans. Circuits Syst. Video Technol., vol. 4, pp. 317-327, June 1994.
  106. P. Cicconi and H. Nicolas, "Efficient region-based motion esti- mation and symmetry oriented segmentation for image sequence coding," IEEE Trans. Circuits Syst. Video Technol., vol. 4, pp. 357-364, June 1994.
  107. V. Garcia-Garduno and C. Labit, "On the tracking of regions over time for very low bat rate image sequence coding," in Proc. Picture Coding Symposium (PCS), Sacramento, CA, Sept. 1994, pp. 257-260.
  108. P. Strobach, "Tree-structured scene adaptive coder," IEEE Trans. Commun., vol. 38, pp. 477-486, Apr. 1990.
  109. L. Lu and W. A. Pearlman, "Multi-rate image sequence coding with quadtree segmentation and backward motion compensa- tion," in Proc. SPIE Conf. Visual Communications and Image Processing (VCIP), Nov. 1992, vol. 1818, pp. 606-614.
  110. K. Asai, Y. Yamada, and T. Murakami, "Video sequence coding based on segment-model and priority control," in Proc. Picture Coding Symposium (PCS), Sacramento, CA, Sept. 1994, pp. 325-328.
  111. T. Ebrahimi, "A new technique for motion field segmentation and coding for very low bitrate video coding applications," in Proc. IEEE Int. Conf. Image Processing (ICIP), Nov. 1994, vol. II, pp. 433-437.
  112. F. Lavagetto, F. Cocurullo, and S. Curinga, "Texture approx- imation through discrete legendre polynomials," in Proc. Very Low Bitrate Video Workshop (VLBV), Essex, U.K., 1994, no. 6.5.
  113. H. H. Chen, M. R. Civanlar, and B. G. Haskell, "A block transform coder for arbitrarily shaped image segments," in Proc. IEEE Int. Conf. Image Processing (ICIP), Nov. 1994, vol. I, pp. 85-89.
  114. M. Gilge, T. Engelhardt, and R. Mehlan, "Coding of arbitrarily shaped image segments based on a generalized orthogonal transform," Signal Process., vol. 1, no. 2, pp. 153-180, Oct. 1989.
  115. M. Kocher and R. Leonardi, "Adaptive region growing tech- nique using polynomial functions for image approximations," Signal Process., vol. 11, no. 1, pp. 47-60, July 1986.
  116. E. Reusens, "Sequence coding based on the fractal theory of iterated transformation systems," in Proc. SPIE Conf. Visual Communications and Image Processing (VCIP), Nov. 1993, vol. 1, pp. 132-140.
  117. M. P. Queluz and B. Macq, "A split-and-merge motion es- timation/compensation technique for very-low bitrate image coding," in Proc. Very Low Bitrate Video Workshop (VLBV), Essex, U.K., 1994, no. 5.2.
  118. T. Chen, C. T. Swain, B. G. Haskell, "An approach to region coding for content-based scalable video," in Proc. IEEE Int. Conf. Image Processing (ICIP), Lausanne, Switzerland, Sept. 1996, vol. III, pp. 399-402.
  119. K. Aizawa, H. Harashima, and T. Saito, "Model-based analysis synthesis image coding (MBASIC) system for a person's face," Signal Process., vol. 1, no. 2, pp. 139-152, Oct. 1989.
  120. T. S. Huang, S. C. Reddy, and K. Aizawa, "Human facial motion modeling, analysis, and synthesis for video compres- sion," in Proc. SPIE Conf. Visual Communications and Image Processing, Nov. 1991, vol. 1605, pp. 234-241.
  121. H. Li and R. Forchheimer, "Two-view facial movement esti- mation," IEEE Trans. Circuits Syst. Video Technol., vol. 4, June 1994.
  122. D. Pearson, "Developments in model-based video coding," Proc. IEEE, vol. 83, pp. 892-906, June 1995.
  123. P. K. Doenges, T. K. Capin, F. Lavagetto, J. Ostermann, I. S. Pandzic, and E. D. Petajan, "MPEG-4: Audio/video and synthetic graphics/audio for mixed media," Signal Process., vol. 9, no. 4, pp. 433-464, May 1997.
  124. J. M. Beaumont, "Image data compression using fractal tech- niques," Br. Telecommun. Technol. J., vol. 9, no. 4, pp. 93-109, Oct. 1991.
  125. H. Li, M. Novak, and R. Forchheimer, "Fractal-based image sequence compression scheme," Opt. Eng., vol. 32, no. 7, pp. 1588-1595, July 1993.
  126. B. Hurtgen and P. Buttgen, "Fractal approach to low rate video coding," in Proc. SPIE Conf. Visual Communications and Image Processing (VCIP), Nov. 1993, vol. 1, pp. 120-131.
  127. M. S. Lazar and L. T. Bruton, "Fractal-based image sequence compression scheme," IEEE Trans. Circuits Syst. Video Tech- nol., vol. 4, pp. 297-308, June 1994.
  128. M. Mattavelli and A. Nicoulin, "Pre and post processing for very low bit-rate video coding," in Proc. Int. Workshop HDTV, Torino, Italy, Oct. 1994.
  129. M. I. Sezan, M. K. Ozkan, and S. V. Fogel, "Temporally adaptive filtering of noisy image sequences using a robust mo- tion estimation algorithm," in Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP), May 1991, vol. IV, pp. 2429-2432.
  130. Y. T. Tse and R. L. Baker, "Global zoom/pan estimation and compensation for video compression," in Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP), May 1991, vol. IV, pp. 2725-2728.
  131. M. Hoetter, "Differential estimation of the global motion pa- rameters zoom and pan," Signal Process., vol. 16, pp. 249-265, 1989.
  132. S. F. Wu and J. Kittler, "A differential method for simultaneous estimation of rotation, change of scale, and translation," Signal Process., vol. 2, pp. 69-80, 1990.
  133. C. Horne, "Improving block based motion estimation by the use of global motion," in Proc. SPIE Conf. Visual Communications and Image Processing (VCIP), 1993, vol. 2094, pp. 576-587.
  134. H. C. Reeve and J. S. Lim, "Reduction of blocking effects in image coding," Opt. Eng., vol. 23, no. 1, pp. 34-37, 1984.
  135. B. Ramamurthi and A. Gersho, "Nonlinear space-variant post- processing of block coded images," IEEE Trans. Acoustics, Speech, Signal Processing, vol. 34, pp. 1258-1268, Oct. 1986.
  136. R. L. Stevenson, "Reduction of coding artifacts in transform image coding," in Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP), Apr. 1993, vol. V, pp. 401-404.
  137. L. Yan, "A nonlinear algorithm for enhancing low bit-rate coded motion video sequence," in Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP), Nov. 1994, vol. II, pp. 923-927.
  138. Y. Yang, N. P. Galatsanos, and A. K. Katsaggelos, "Iterative projection algorithms for removing the blocking artifacts of block-DCT compressed images," in Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP), Apr. 1993, vol. V, pp. 401-408.
  139. B. Macq, M. Mattavelli, O. Van Calster, E. van der Plancke, S. Comes, and W. Li, "Image visual quality restoration by cancellation of the unmasked noise," in Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP), 1994, vol. V, pp. 53-56.
  140. T. Chen, "Elimination of subband-coding artifacts using the dithering technique," in Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP), Nov. 1994, vol. II, pp. 874-877.
  141. W. Li, O. Egger, and M. Kunt, "Efficient quantization noise reduction device for subband image coding schemes," in Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP), Detroit, MI, May 1995, vol. IV, pp. 2209-2212.
  142. A. Gersho and R. M. Gray, Vector Quantization and Signal Compression. Norwell, MA: Kluwer, 1992.
  143. Y. Linde, A. Buzo, and R. M. Gray, "An algorithm for vector quantizer design," IEEE Trans. Commun., vol. COM-28, pp. 84-95, Jan. 1980.
  144. R. M. Gray, "Vector quantization," IEEE Acoust., Speech, Signal Processing Mag., vol. 1, pp. 4-29, Apr. 1984.
  145. H. Abut, Vector Quantization. New York: IEEE Press, 1990.
  146. A. N. Netravali and B. G. Haskell, Digital Pictures Representa- tion, Compression, and Standards, 2nd ed. New York: Plenum, 1995.
  147. C. E. Shannon, "A mathematical theory of communication," Bell Syst. Tech. J., vol. 27, pp. 379-423, 1948.
  148. T. Berger, "Optimum quantizers and permutation codes," IEEE Trans. Inform. Theory, vol. IT-18, pp. 759-765, Nov. 1972.
  149. I. Moccagatta and M. Kunt, "Lattice vector quantization ap- proach to image coding," in Proc. SPIE Visual Communications and Image Processing '92, Boston, MA, 1992.
  150. T. Chen, H. P. Graf, H. Chen, W. Chou, B. G. Haskell, E. Petajan, and Y. Wang, "Lip synchronization in talking head video utilizing speech information," in Proc. SPIE Conf. Visual Communication and Image Processing, Taipei, Taiwan, May 1995.
  151. R. Koenen, "MPEG-7: Context and objectives version 5.0," ISO/IEC JTC1 SC29/WG11 N1920, Fribourg, Switzerland, Oct. 1997.
  152. M. R. Banham and J. C. Brailean, "A selective update approach to matching pursuits video coding," IEEE Trans. Circuits Syst. Video Technol., vol. 7, pp. 119-129, Feb. 1997.
  153. R. Neff and A. Zakhor, "Very low bit-rate video coding based on matching pursuits," IEEE Trans. Circuits Syst. Video Technol., vol. 7, pp. 158-171, Feb. 1997.
  154. M.-C. Lee, W. Chen, C. B. Lin, C. Gu, T. Markoc, S. I. Zabrinsky, and R. Szelinski, "A layered video object coding system using sprite and affine motion model," IEEE Trans. Circuits Syst. Video Technol., vol. 7, pp. 130-145, Feb. 1997.
  155. E. Reusens, T. Ebrahimi, C. Le Buhan, R. Castagno, V. 1124 PROCEEDINGS OF THE IEEE, VOL. 86, NO. 6, JUNE 1998

Related papers

High-Performance Compression of Visual Information - A Tutorial Review - Part I: Still Pictures
Murat Kunt

2001

Digital images have become an important source of information in the modern world of communication systems. In their raw form, digital images require a tremendous amount of memory. Many research e orts have been devoted to the problem of image compression in the last two decades. Two di erent compression categories must be distinguished: lossless and lossy. Lossless compression is achieved if no distortion is introduced in the coded image. Applications requiring this type of compression include medical imaging and satellite photography. For applications such as video{telephony or multimedia applications some loss of information is usually tolerated in exchange for a high compression ratio. In this two-part paper, the major building blocks of image coding schemes are overviewed. Part I covers still image coding, and Part II covers motion picture sequences. In this rst part, still image coding schemes have been classi ed into predictive, block transform and multi-resolution approaches. Predictive methods are suited to lossless and low compression applications. Transform based coding schemes achieve higher compression ratios for lossy compression but su er from blocking artifacts at high compression ratios. Multi-resolution approaches are suited for lossy as well for lossless compression. At lossy high compression ratios the typical artifact visible in the reconstructed images is the ringing e ect. New applications in a multimedia environment drove the need for new functionalities of the image coding schemes. For that purpose, second{generation coding techniques segment the image into semantically meaningful parts. Therefore, parts of these methods have been adapted to work for arbitrarily{shaped regions. In order to add another functionality such as progressive transmission of the information, speci c quantization algorithms must be de ned. A nal step in the compression scheme is achieved by the codeword assignment. Finally, coding results are presented which compare state of the art techniques for lossy and lossless compression. The di erent artifacts of each technique are highlighted and discussed. Also, the possibility of progressive transmission is illustrated.

View PDFchevron_right
High-performance compression of visual information-a tutorial review. i. still pictures
Touradj Ebrahimi

1999

Abstract Digital images have become an important source of information in the modern world of communication systems. In their raw form, digital images require a tremendous amount of memory. Many research efforts have been devoted to the problem of image compression in the last two decades. Two different compression categories must be distinguished: lossless and lossy. Lossless compression is achieved if no distortion is introduced in the coded image.

View PDFchevron_right
IMAGE COMPRESSION METHODS BASED ON TRANSFORM CODING AND FRACTAL CODING
IJESRT Journal

Image compression is process to remove the redundant information from the image so that only essential information can be stored to reduce the storage size, transmission bandwidth and transmission time. The essential information is extracted by various transforms techniques such that it can be reconstructed without losing quality and information of the image. In this research comparative analysis of image compression is done by four transform method, which are Discrete Cosine Transform (DCT), Discrete Wavelet Transform(DWT) & Hybrid (DCT+DWT) Transform and fractal coding. MATLAB programs were written for each of the above method and concluded based on the results obtained that hybrid DWT-DCT algorithm performs much better than the standalone JPEG-based DCT, DWT algorithms in terms of peak signal to noise ratio (PSNR), as well as visual perception at higher compression ratio. The popular JPEG standard is widely used in digital cameras and web based image delivery. The wavelet transform, which is part of the new JPEG 2000 standard, claims to minimize some of the visually distracting artifacts that can appear in JPEG images. For one thing, it uses much larger blocks-selectable, but typically 1024 x 1024 pixels for compression, rather than the 8 X 8 pixel blocks used in the original JPEG method, which often produced visible boundaries. Fractal compression has also shown promise and claims to be able to enlarge images by inserting realistic detail beyond the resolution limit of the original.

View PDFchevron_right
Image Compression
Yorgos Andreadakis

1993

The evolution over the last decade in many fields of digital technology - especially for image acquisition, storage and display - have brought about many applications of digital imaging. However, due to their relatively high cost these applications are not commonplace in general purpose computer systems the way text, and geometric graphics are. The main obstacle for this type of applications is the vast amount of information needed to represent a digital image. A single digitised colour picture at TV resolution has a size of around 1 million bytes, and 35mm resolution requires ten times that amount. A 640 x 400 pixel 24-bit true colour still image requires 768Kb of disk space, while motion video requires 22Mb per second i.e. 1.3 Gbytes per minute. Why do we need image compression ? Graphics standards continue to evolve at an increasing rate. The trend is towards true-colour (24-bit) displays on PCs, as already available on Mac and Sun platforms. However, display technology has advanced much faster than data storage technology. In 1983, the floppy disk format was 320 Kb. Now in 1993, the standard is 1.44 Mb. That is a four times increase in storage capacity in 10 years. Over the same period we have gone from CGA displays with 320 x 200 resolution at 2 bits per pixel and 16 Kbytes full-screen file size to Super VGA with 1024x768 x 8-bit displays and 768Kb file size. That is a 48 times growth. Furthermore we now have Targa 24-bit displays with 640x400 x 24-bit and SUN SPARCstation displays at 1280x1024 x 24-bit. The latter is a 240 times increase over CGA's full screen storage requirements in the last 10 years. This pace of display advancement will increase with storage and transmission lagging behind therefore image compression is going to become increasingly important. It is therefore obvious from the above mentioned that the storage needs of digital images are enormous as well as the image transmission time over a network or a modem is great. Thus the need for image compression. What is this thesis about ? This thesis is an investigation amongst the most popular ways of compressing still colour images. Two methods have been selected, their mathematical background is described and their compression ratios and image quality after compression, are compared. The methods selected utilise two currently evolving standards: the Joint Photographic Experts Group (JPEG) standard for still picture compression and the Fractal Image Compression. This thesis is neither an attempt to implement the described standards nor an effort to improve existing algorithms. I have managed to come out with a program implementing partially the JPEG standard by linking various routines that I have found, but it should not be considered that this is the optimal implementation. It has not been possible to find any algorithms implementing the Fractal compression because it the method is protected under the copyright of Iterated Systems. The actual comparison of the two standards will be based on information issued by Iterated Systems about the ratios achieved by their implementation. It will be noticed that more attention has been given to the JPEG compression method. This is because it was not possible to find more material about Fractal Compression due to the unavailability of time. It is felt that with more time we would be able to describe Fractal compression in greater detail.

View PDFchevron_right
A Review on Image Compression Techniques
Krishna Marlapalli

The demand for images, video sequences and computer animations has increased drastically over the years. This has resulted in image and video compression becoming an important issue in reducing the cost of data storage and transmission. JPEG is currently the accepted industry standard for still image compression, but alternative methods are also being explored. Fractal Image Compression (FIC) is one of them. This scheme works encoding by partitioning an image into blocks and using Contractive Mapping to map range blocks to domains. The encoding step in fractal image compression has high computational complexity whereas, decoding step involves starting from all zeros image to achieve final image which is same as original image by applying self Transformations.

View PDFchevron_right

Related topics

  • Biomedical Engineering
  • Data Compression
  • Video Compression
  • Image compression
  • Motion compensation
  • Compression Algorithms
  • Human Visual System
  • Multimedia Application
  • Electrical and Electronic Engine...
  • Application Software
  • Compression Algorithm
    • 580 California St., Suite 400
    San Francisco, CA, 94104
    © 2025 Academia. All rights reserved