Modified Huffman Algorithm for Image Encoding and Decoding

Authors(3) :-Sona Khanna, Suman Kumari, Tadir

Lossless compression of a progression of symbols is a decisive part of data and signal compression. Huffman coding is lossless in nature; it is also generally utilized in lossy compression as the eventual step after decomposition and quantization of a signal. In signal compression, the disintegration and quantization part seldom manages to harvest a progression of completely autonomous symbols. Here we present a schema giving prominent results than forthright Huffman coding by exploiting this fact. We cleft the inceptive symbol sequence into two arrangements in such a way that the symbol statistics are, sanguinely, different for the two possessions. Sole Huffman coding for each of these disposition will reduce the average bit rate. This split is done recursively for each arrangement until the cost league with the split is larger than the attainment. Assay was done on distinct signals. The harvest using the cleft schema was a bit rate devaluation of ordinarily besides than 10% compared to forthright Huffman coding, and 0- 15% surpassing than JPEG-like Huffman coding, inimitable at low bit rates.

Authors and Affiliations

Sona Khanna
Department of Computer Science and Engineering, Guru Nanak Dev University RC, Gurdaspur, India
Suman Kumari
Department of Computer Science and Engineering, Guru Nanak Dev University RC, Gurdaspur, India
Department of Computer Science and Engineering, Guru Nanak Dev University RC, Gurdaspur, India

Lossless Compression, Huffman Coding, Disintegration

  1. Allen Gersho and Robert M. Gray. Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston, 1992. ISBN 0-7923-9181-0
  2. Massachutilizetts Institute of Technology. The MIT-BIH Arrhythmia Database CD-ROM, 22nd edition, 1992.
  3. Mark Nelson, Jean-Loup Gailly. The Data Compression Book. M&T Books, New York, USA, 1996. ISBN 1-55851-434-1
  4. William B. Pennebaker, Joan L. Mitchell. JPEG: Still Image Data Compression Standard. Van Nostrand Reinhold, New York, USA, 1992. ISBN: 0442012721
  5. Seismic Data Compression ReferenceSet.
  6. N. Kaur, “A Review of Image Compression Using Pixel Correlation & Image Decomposition with,” vol. 2, no. 1, pp. 182–186, 2013.
  7. A. S. Arif, S. Mansor, R. Logeswaran, and H. A. Karim, “Auto-shape Lossless Compression of Pharynx and Esophagus Fluoroscopic Images,” J. Med. Syst., vol. 39, no. 2, pp. 1–7, 2015.
  8. W. M. Abd-elhafiez and W. Gharibi, “Color I mage C ompression A lgorithm B ased on the DCT B locks,” vol. 9, no. 4, pp. 323–328, 2012.
  9. R. a.M, K. W.M, E. M. a, and W. Ahmed, “Jpeg Image Compression Using Discrete Cosine Transform - A Survey,” Int. J. Comput. Sci. Eng. Surv., vol. 5, no. 2, pp. 39–47, 2014.
  10. G. Badshah, S. Liew, J. M. Zain, S. I. Hisham, and A. Zehra, “Importance of Watermark Lossless Compression in Digital Medical Image Watermarking,” vol. 4, no. 3, pp. 75–79, 2015.
  11. S. Stolevski, “Hybrid PCA Algorithm for Image Compression,” pp. 685–688, 2010.
  12. R. C. . Gonzalez and R. E. Woods, “Digital Image Processing,” p. 976, 2010.
  13. D. G. Sullivan and D. Ph, “Binary Trees and Huffman Encoding Binary Search Trees Fall 2012,” 2012.
  14. T. Gebreyohannes and D. Kim, “a Daptive Noise Reduction Scheme for Salt and Pepper.”
  15. R. H. Chan, C.-W. Ho, and M. Nikolova, “Salt-and-Pepper noise removal by median-type noise detectors and detail-preserving regularization.,” IEEE Trans. Image Process., vol. 14, no. 10, pp. 1479–85, 2005.
  16. S. Kaisar and J. Mahmud, “Salt and Pepper Noise Detection and removal by Tolerance based selective Arithmetic Mean Filtering Technique for image restoration,” Ijcsns, vol. 8, no. 6, pp. 271–278, 2008.
  17. G. Nelapati, “ISSN No . 2278-3091 International Journal of Advanced Trends in Computer Science and Engineering Available Online at Salt and Pepper Noise Detection and removal by Modified Decision based Unsymmetrical Trimmed Med,” vol. 1, no. 2278, pp. 93–97, 2012.
  18. A. Singh, U. Ghanekar, C. Kumar, G. Kumar, and N. I. T. Kurukshetra, “An Efficient Morphological Salt-and-Pepper Noise Detector,” vol. 875, pp. 873–875, 2011.
  19. X. Zhang, F. Ding, Z. Tang, and C. Yu, “Salt and pepper noise removal with image inpainting,” AEU - Int. J. Electron. Commun., vol. 69, no. 1, pp. 307–313, Jan. 2015.

Publication Details

Published in : Volume 2 | Issue 3 | May-June 2016
Date of Publication : 2016-06-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 444-448
Manuscript Number : IJSRSET16236
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

Sona Khanna, Suman Kumari, Tadir , " Modified Huffman Algorithm for Image Encoding and Decoding, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 2, Issue 3, pp.444-448, May-June-2016.
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