Call for Paper - November 2021 Edition
IJCA solicits original research papers for the November 2021 Edition. Last date of manuscript submission is October 20, 2021. Read More

An Improved JPEG Image Compression Technique based on Selective Quantization

International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 55 - Number 3
Year of Publication: 2012
Mahmud Hasan
Kamruddin Md. Nur
Hasib Bin Shakur

Mahmud Hasan, Kamruddin Md. Nur and Hasib Bin Shakur. Article: An Improved JPEG Image Compression Technique based on Selective Quantization. International Journal of Computer Applications 55(3):9-14, October 2012. Full text available. BibTeX

	author = {Mahmud Hasan and Kamruddin Md. Nur and Hasib Bin Shakur},
	title = {Article: An Improved JPEG Image Compression Technique based on Selective Quantization},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {55},
	number = {3},
	pages = {9-14},
	month = {October},
	note = {Full text available}


In today's communicative and multimedia computing world, JPEG images play a vast consequential role. The JPEG images have been able to satisfy the users by fulfilling their demand of preserving numerous digital images within considerably less storage space. Although the JPEG standard offers four different sorts of compression mechanism, among them the Baseline JPEG or Lossy Sequential DCT Mode of JPEG is most popular since it can store a digital image by temporarily removing its psychovisual redundancy and thereby offering a very less storage space for a large image. Again, the computational complexity of Baseline JPEG is also considerably less as compression takes place in Discrete Cosine Transform domain. Therefore, Baseline JPEG is substantially useful while storing, sharing and transmitting digital images. Despite removing a large amount of psychovisual redundancy, the Baseline JPEG still contains redundant data in DCT domain. This paper explores the fact and introduces an improved technique that modifies the Baseline JPEG algorithm. It describes a way to further compress a JPEG image without any additional loss while achieving a better compression ratio than that is achievable by Baseline JPEG. The contribution of this work is to incorporate a simple mathematical series with Baseline JPEG before applying optimal encoding and perform a Selective Quantization that essentially does not loss any information after decompression but reduces the redundant data in DCT domain. The proposed technique is tested on over 200 textbook images that are extensively used for testing standard Image Processing and Computer Vision algorithms. The experimental results show that our proposed approach achieves 2:15% and 14:10% better compression ratio than that is achieved by Baseline JPEG on an average for gray-scale and true-color images respectively.


  • Steinmetz, R. , Nahrstedt, K. : Multimedia: computing, communications, and applications. Prentice Hall PTR, 1995.
  • Wallace, G. K. :The JPEG still picture compression standard. IEEE Transactions on Consumer Electronics, 38(1):18–34, February 1992.
  • Gonzalez, R. C. ,Woods, R. E. : Digital Image Processing. 2nd ed, p. 520.
  • Pennebaker, W. B. , Mitchell, J. L. : JPEG Still Image Data Compression Standard. Van Nostrand Reinhold, New York, 1993.
  • Miano, J. : Compressed Image File Formats: JPEG, PNG, GIF, XBM, BMP. New York: ACM Press, 1999.
  • Hankerson, D. , Harris, G. A. , JohnsonJr. , P. D. : Introduction to Information Theory and Data Compression. Boca Raton, FL: CRC, 1997.
  • Acharya, T. , Ray, A. K. : Digital Image Processing: Principles and Applications. John Wiley & Sons, Inc. ISBN: 10 0-471- 71998-6, 2005.
  • Wikipedia: The JPEG Codec Example. http://en. wikipedia. org/wiki/JPEG# JPEG codec example.
  • Bauschke, H. H. , Hamilton, C. H. , Macklem, M. S. , McMichael, J. S. , Swart, N. R. : Recompression of JPEG Images by Requantization. IEEE Transactions on Image Processing, vol. 12, no. 7, July 2003.
  • Nelson, M. , Gailly, J. L. : The Data Compression Book. 2nd ed. New York: M & T Books, 1996.
  • Takezawa, M. , Sanada, H. ,Watanabe, K. : Quality Improvement Technique for JPEG Images with Fractal Image Coding. IEEE, 2005.
  • Richter, T. : Visual quality improvement techniques of HDPhoto/JPEG-XR. 15th IEEE International Conference on Image Processing, 2008.
  • Gunawan, I. P. , Halim, A. : Haar wavelet decomposition based blockiness detector and picture quality assessment method for JPEG images. International Conference on Advanced Computer Science and Information System (ICACSIS), 2011.
  • Zhou,W. , Sheikh, H. R. , Bovik, A. C. : No-reference perceptual quality assessment of JPEG compressed images. IEEE International Conference on Image Processing, 2002.
  • Altous, S. , Samee, M. K. , Gotze, J. : Reduced reference image quality assessment for JPEG distortion. ELMAR, 2011 Proceedings.
  • Gastaldo, P. , Zunino, R. : No-reference quality assessment of JPEG images by using CBP neural networks. International Symposium on Circuits And Systems (ISCAS), 2004.
  • Stirner, M. , Seelmann, G. : Improved Redundancy Reduction for JPEG Files. Picture Coding Symposium by EURASIP, 2007. ISBN: 978-989-8109-05-7.
  • Bauermann, I. , Steinbach, E. : Further Lossless Compression of JPEG Images. Proc. of Picture Coding Symposium, San Fransisco, USA, Dec 15-17, 2004.
  • Matsuda, I. , Nomoto, Y. , Wakabayashi, K. , Itoh, S. : Lossless Re-Encoding of JPEG Images using Block-Adaptive Intra Prediction. 16th European Signal Processing Conference (EUSIPCO 2008), Lausanne, Switzerland, August 25- 29, 2008.
  • Zhong-Hua, Z. , Wen-Yan, W. : A lossless compression method of JPEG file based on shuffle algorithm. 2nd International Conference on Advanced Computer Control (ICACC), 2010.
  • Golner, M. A. , Mikhael, W. B. , Krishnan, V. , Ramaswamy A. : Region based variable quantization for JPEG image compression. Proceedings of the 43rd IEEE Midwest Symposium on Circuits and Systems, 2000.