Comparative analysis of various Image compression techniques for Quasi Fractal lossless compression

  • Satyawati Magar Department of E&TC, D.V.V.P College of Engineering, Ahmednagar, Maharashtra, India.
  • Bhavani Sridharan Department of ECE, Sri Shakthi Institute of Engineering & Technology Coimbatore, Tamil Nadu, India.
Keywords: Adaptive Threshold, Oscillation Concept, BTC-SPIHT, Quasi Fractal, Morphological Filter, CR, PSNR


The most important Entity to be considered in Image Compression methods are Paek to signal noise ratio and Compression ratio. These two parameters are considered to judge the quality of any Image.and they a play vital role in any Image processing applications. Biomedical domain is one of the critical areas where more image datasets are involved for analysis and biomedical image compression is very, much essential. Basically, compression techniques are classified into lossless and lossy. As the name indicates, in the lossless technique the image is compressed without any loss of data. But in the lossy, some information may loss. Here both lossy & lossless techniques for an image compression are used. In this research different compression approaches of these two categories are discussed and brain images for compression techniques are highlighted. Both lossy and lossless techniques are implemented by studying it’s advantages and disadvantages. For this research two important quality parameters i.e. CR & PSNR are calculated. Here existing techniques DCT, DFT, DWT & Fractal are implemented and introduced new techniques i.e Oscillation Concept method, BTC-SPIHT & Hybrid technique using adaptive threshold & Quasi Fractal Algorithm.


Metrics Loading ...


C. Taskin, S.K. Sarikoz, (2008) An overview of image compression approaches, In 2008 The Third International Conference on Digital Telecommunications, IEEE, 174-179.

M. Abo–Zahhad, R.R. Gharieb, S.M. Ahmed, M. Khaled, Brain Image Compression Techniques, International Journal of Engineering Trends and Technology, 19 (2015) 93-105.

G. Vijayvargiya, S. Silakari, R. Pandey, A Survey: Various Techniques of Image Compression, International Journal of Computer Science and Information Security, 11 (2013).

S. Liang, G. Wang, S. Wang, Y. Wang, A New Method of Image Quality Assessment, Wseas Transactions on Signal Processing, 12 (2016) 94-101.

M.S. Ibraheem, S.Z. Ahmed, K. Hachicha, S. Hochberg, P. Garda, (2016) Medical images compression with clinical diagnostic quality using logarithmic DWT, In 2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), IEEE, 402-405.

M. Abo-Zahhad, R.R. Gharieb, S.M. Ahmed, M.K. Abd-Ellah, Huffman image compression incorporating DPCM and DWT, Journal of Signal and Information Processing, 6 (2015) 123.

K. Prachumrak, A. Hiramatsu, T. Fuchida, H. Nakamura, S. Murashima, Lossless fractal image coding, In Proceedings EC-VIP-MC 2003, 4th EURASIP Conference focused on Video/Image Processing and Multimedia Communications (IEEE Cat. No. 03EX667) IEEE, 1 (2003) 95-100.

S. Bhavani, K. Thanushkodi, Neural Based Domain and Range Pool Partitioning Using Fractal Coding for Nearly Lossless medical Image Compress, WSEAS Transactions on signal Processing, 1 (2013) 11-20.

R. Sharma, C.R Kamargaonkar, M. Sharma, Hybrid Medical Image Compression: Survey, International Journal of Advanced Research in Computer Engineering and Technology, 5 (2016) 1036-1038.

H. Alyaa, Medical Image Data Compression Using Hybrid Methods, ARPN Journal of Engineering and Applied Sciences, 13 (2018) 1877-1886.

B. Nacera, B. Soumia, (2011) A hybrid scheme coding using SPHIT and fractal for mammography image compression, In 2011 15th International Conference on Information Visualisation, IEEE, 534-534.

N. Markandeya, S. Patil, (2017) Digital image compression hybrid technique based on block truncation coding and discrete cosine transform, In 2017 International Conference on Trends in Electronics and Informatics (ICEI), IEEE, 1159-1162.

H. Singh, S. Rana, Image Compression Hybrid using DCT, DWT, Huffman, International Journal of Scientific and Engineering Research, 3 (2012) 1-4.

S.E. Ghrare, A.R. Khobaiz, (2014) Digital image compression using block truncation coding and Walsh Hadamard transform hybrid technique, In 2014 International Conference on Computer, Communications, and Control Technology (I4CT), IEEE, 477-480.

G. Soundarya, S. Bhavani, Comparison of hybrid codes for MRI brain image compression, Research Journal of Applied Sciences, Engineering and Technology, 4 (2012) 5367-5371.

S. Mathew, S. Sebastian, Image Compression by using Morphological Operations and Edge-Based Segmentation Technique, International Journal of Advanced Research in Computer and Communication Engineering, 4 (2015) 73-76.

P.K. Saini, M. Singh, Brain tumor detection in medical imaging using MATLAB, International Research Journal of Engineering and Technology, 2 (2015) 191-196.

J. Yang, D. Park, (2004) Detecting region-of-interest (RoI) in digital mammogram by using morphological bandpass filter, In 2004 IEEE International Conference on Multimedia and Expo (ICME) (IEEE Cat. No. 04TH8763), IEEE, 2 (2004) 1279-1282.

T. Kalaiselvi, S. Boopathiraja, P. Sriramakrishnan, ROI Based Hybrid Compression Techniques for Transferring MRI Brain Images, International Journal of Computer Science Trends and Technology, 4 (2016) 270-277.

M. Gupta, A.K. Garg, Analysis of image compression algorithm using DCT, International Journal of Engineering Research and Applications, 2 (2012) 515-521.

A. Kaur, J. Kaur, Comparison of DCT and DWT of Image Compression Techniques, International journal of engineering research and development, 1 (2012) 49-52.

R.N. Chaudhary, (2010) Waves and Oscillations, New Edge International Publishers, India.

Stephan Chaphman, (2012) Matlab Programming for Engineers, Cengage Learning Publishers, USA.

R.C. Gonzalez, R.E. Woods, (2004) Digital Image Processing, Pearson Education, International, UK.

R.C. Gonzalez, R.E. Woods, S. Eddins, (2004) Digital image Processing using MATLAB, Pearson Education International, UK.

How to Cite
Magar, S., & Sridharan, B. (2020). Comparative analysis of various Image compression techniques for Quasi Fractal lossless compression. International Journal of Computer Communication and Informatics, 2(2), 30-45.

Views: Abstract : 11 | PDF : 5

Plum Analytics