Skip to content


  • Research Article
  • Open Access

A Comparison of Set Redundancy Compression Techniques

EURASIP Journal on Advances in Signal Processing20062006:092734

  • Received: 27 February 2005
  • Accepted: 21 January 2006
  • Published:


Medical imaging applications produce large sets of similar images. Thus a compression technique is necessary to reduce space storage. Lossless compression methods are necessary in such critical applications. Set redundancy compression (SRC) methods exploit the interimage redundancy and achieve better results than individual image compression techniques when applied to sets of similar images. In this paper, we make a comparative study of SRC methods on sample datasets using various archivers. We also propose a new SRC method and compare it to existing SRC techniques.


  • Information Technology
  • Medical Imaging
  • Quantum Information
  • Space Storage
  • Image Compression

Authors’ Affiliations

Institut National d'Informatique (INI), BP 68M, Oued Smar, Algiers, 16270, Algeria


  1. Bekkouche H, Barret M: Adaptive multiresolution decomposition: application to lossless image compression. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '02), May 2002, Orlando, Fla, USAGoogle Scholar
  2. Celik MU, Sharma G, Tekalp AM: Gray-level embedded lossless image compression. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '03), April 2003, Hong Kong III-245–III-248.Google Scholar
  3. Chang CC, Chen GI: Enhancement algorithm for nonlinear context-based predictors. IEE Proceedings - Vision, Image, and Signal Processing 2003, 150(1):15–19. 10.1049/ip-vis:20030163View ArticleGoogle Scholar
  4. Clunie DA: Lossless compression of grayscale medical images: effectiveness of traditional and state-of-the-art approaches. Medical Imaging 2000: PACS Design and Evaluation: Engineering and Clinical Issues, February 2000, San Diego, Calif, USA, Proceedings of SPIE 3980: 74–84.Google Scholar
  5. Jiang J, Guo B, Yang SY: Revisiting the JPEG-LS prediction scheme. IEE Proceedings: Vision, Image and Signal Processing 2000, 147(6):575–580. 10.1049/ip-vis:20000767Google Scholar
  6. Karadimitriou K: Set redundancy, the enhanced compression model, and methods for compressing sets of similar images, M.S. thesis. Department of Computer Science, Louisiana State University, Baton Rouge, La, USA; August 1996.Google Scholar
  7. Huffman DA: A method for the construction of minimum redundancy codes. Proceedings of IRE 1952, 40(9):1098–1101.View ArticleGoogle Scholar
  8. Shkarin D: Improving the efficiency of PPM algorithm. Problems of Information Transmission 2001, 37(3):226–235(10). 10.1023/A:1013878007506MathSciNetView ArticleGoogle Scholar
  9. Ziv J, Lempel A: A universal algorithm for sequential data compression. IEEE Transactions on Information Theory 1977, 23(3):337–343. 10.1109/TIT.1977.1055714MathSciNetView ArticleGoogle Scholar
  10. Ziv J, Lempel A: Compression of individual sequences via variable-rate coding. IEEE Transactions on Information Theory 1978, 24(5):530–536. 10.1109/TIT.1978.1055934MathSciNetView ArticleGoogle Scholar
  11. Neter J, Wasserman W, Kutner MH: Applied Linear Regression Models. IRWIN, Burr Ridge, Ill, USA; 1989.Google Scholar
  12. Karadimitriou K, Tyler JM: The min-max differential method for large-scale storage and compression of medical images. Proceedings of of Annual Molecular Biology and Biotechnology Conference, 1996, Baton Rouge, La, USAGoogle Scholar
  13. Karadimitriou K, Tyler JM: Min-max compression methods for medical image databases. ACM SIGMOD Record 1997, 26(1):47–52. 10.1145/248603.248613View ArticleGoogle Scholar
  14. Karadimitriou K, Tyler JM: The Centroid method for compressing sets of similar images. Pattern Recognition Letters 1998, 19(7):585–593. 10.1016/S0167-8655(98)00033-6View ArticleGoogle Scholar
  15. El-Sonbaty Y, Hamza M, Basily G: Compressing sets of similar medical images using multilevel centroid technique. In Processing of the 7th Conference on Digital Image Computing, Techniques and Applications, December 2003, Sydney, Australia Edited by: Sun C, Talbot H, Ourselin S, Adriaansen T.Google Scholar
  16. Lee JD, Wan SY, Wu RF: A hybrid compression model for clusters of similar medical images. Biomedical Engineering - Aplications, Basis & Communications 2003., 16(1):Google Scholar
  17. Weinberger MJ, Seroussi G, Sapiro G: LOCO-I: a low complexity, context-based, lossless image compression algorithm. Proceedings of the IEEE Data Compression Conference, April 1996, Snowbird, Utah, USA ISO Working Document ISO/IEC JTC1/SC29/WG1 N203Google Scholar
  18. Weinberger MJ, Seroussi G, Sapiro G: The LOCO-I lossless image compression algorithm: principles and standardization into JPEG-LS. IEEE Transactions on Image Processing 2000, 9(8):1309–1324. 10.1109/83.855427View ArticleGoogle Scholar


© Ait-Aoudia and Gabis. 2006