IJSTR

International Journal of Scientific & Technology Research

IJSTR@Facebook IJSTR@Twitter IJSTR@Linkedin
Home About Us Scope Editorial Board Blog/Latest News Contact Us
CALL FOR PAPERS
AUTHORS
DOWNLOADS
CONTACT
QR CODE
IJSTR-QR Code

IJSTR >> Volume 1 - Issue 2, March 2012 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



A Lossless Image Compression Technique using Location Based Approach

[Full Text]

 

AUTHOR(S)

Mahmud Hasan, Kamruddin Md. Nur

 

KEYWORDS

Bits Per Pixel, Frequency Distribution, Image Differencing, Location Preserving, Lossless Image Compression, Mean Square Error, Most Frequent Pixel.

 

ABSTRACT

In modern communicative and networked computing, sharing and storing image data efficiently have been a great challenge. People all over the world are sharing, transmitting and storing millions of images every moment. Although, there have been significant development in storage device capacity enhancement sector, production of digital images is being increased too in that proportion. Consequently, the demand of handsome image compression algorithms is yet very high. Easy and less-time-consuming transmission of high quality digital images requires the compression-decompression (CODEC) technique to be as simple as possible and to be completely lossless. Keeping this demand into mind, researchers around the world are trying to innovate such a compression mechanism that can easily reach the goal specified. After a careful exploration of the existing lossless image compression methods, we present a computationally simple lossless image compression algorithm where the problem is viewed from a different angle- as the frequency distribution of a specific gray level over a predefined image block is locatable, omission of the most frequent pixel from the block helps achieve better compression in most of the cases. Introducing the proposed algorithm step by step, a detailed worked out example is illustrated. The performance of the proposed algorithm is then measured against some standard image compression parameters and comparative performances have been considered thereafter. It has been shown that our approach can achieve about 4.87% better compression ratio as compared to the existing lossless image compression schemes.

 

REFERENCES

[1] Ralf Steinmetz and Klara Nahrstedt, Multimedia: Computing, Communications and Applications, 1st Edition, Pearson Education Inc. ISBN: 81-7808-319-1, 2005.
[2] Rafael C. Gonzalez and Richard E. Woods, Digital Image Processing, 2nd Edition, Pearson Prentice Hall. ISBN: 81-7758-168-6, 2005.
[3] Tinku Acharya and Ajoy K. Ray, Digital Image Processing: Principles and Applications, John Wiley & Sons, Inc. ISBN: 10 0-471-71998-6, 2005.
[4] M. Nelson and J. L. Gailly, The Data Compression Book, 2nd ed. New York: M & T Books, 1996.
[5] Gregory K. Wallace, The JPEG Still Picture Compression Standard, IEEE Transactions on Consumer Electronics, 1991.
[6] Pennebaker WB and Mitchell JL., JPEG still image data compression standard, Van Nostrand Reinhold; 1993.
[7] Jan-Yie Liang, Chih-Sheng Chen, Chua-Huang Huang and Li Liu, Lossless Compression of Medical Images using Hilbert space-filling Curves, Computerized Medical Imaging and Graphics-32, pp. 174-182., 2008.
[8] Sayood K., Introduction to data compression, 2nd ed. Moorgan Kaufmann; 1991.
[9] Lu Zhang, Bingliang Hu, Yun Li and Weiwei Yu, An Algorithm for Moving Multi-target Prediction in a Celestial Background, Communications in Computer and Information Science (CCIS) 61, pp 41-47, 2009.
[10] Sunil Kumar Pattanik, K. K. Mahapatra and G. Panda, A Novel Lossless Image Compression Algorithm using Arithmetic Modulo Operation, IEEE International Conference on Cybernetics & Intelligence Systems (CIS) and Robotics Automation & Mechatronics (RAM) (CIS-RAM 2006), Thailand, pp. 234-238, 2006.
[11] Komal Ramteke and Sunita Rawat, Lossless Image Compression LOCO-R Algorithm for 16 bit Image, 2nd National Conference on Information and Communication Technology (NCICT), pp. 11-14, 2011.
[12] Syed Ali Hassan and Mehdi Hussain, Spatial Domain Lossless Image Data Compression Method, International Conference of Information and Communication Technologies, 2011.
[13] Al-Wahaib and M. S. KokSheikh Wong, A Lossless Image Compression Algorithm Using Duplication Run Length Coding, IEEE Conference on Network Application Protocols and Services, pp. 245-250, 2010.
[14] C. Saravanan and R. Ponalagusamy, Lossless Grey-Scale Image Compression Using Source Symbol Reduction and Huffman Coding, International Journal of Image Processing, IJIP, Vol-3. Issue-5, pp. 246-251,2009.
[15] Kubasova, O. and Toivanen, P., Lossless Compression Methods for Hyperspectral Images, International Conference on Pattern Reognition (ICPR), 2004.
[16] Sheng-Chieh Huang, Liang-Gee Chen and Hao-Chieh Chang, A Novel Image Compression Algorithm by Using LOG-EXP Transform,
[17] Jacob Ziv and Abraham Lempel, A Universal Algorithm for Sequential Data Compression, IEEE Transaction on Information Theory (23-3), pp.337-343, 1977.
[18] Belloulata, K., Stasinski, R. and Konrad, J., Region-based image compression using fractals and shape-adaptive DCT, International Conference on Image Processing, pp. 815-819, 1999.
[19] Kamel Belloulata and Janusz Konrad, Fractal Image Compression with Region-Based Functionality, IEEE Transaction on Image Processing, Vol-11, No-4, 2002.
[20] Hannes Hartenstein, Matthias Ruhl and Dietmar Saupe, Region-Based Fractal Image Compression, IEEE Transaction on Image Processing, Vol-9, No-7, 2000.