Fractal Image Compression Based on High Entropy Values Technique

Authors

  • Douaa Younis Abbaas Departement of Computer Science, College of Science, Mustansiriyah University, IRAQ.

DOI:

https://doi.org/10.23851/mjs.v28i2.507

Keywords:

Fractal Image Compression, Entropy, Image Quality, Domain Pool, Similarity, En-coding

Abstract

There are many attempts tried to improve the encoding stage of FIC because it consumed time. These attempts worked by reducing size of the search pool for pair range-domain matching but most of them led to get a bad quality, or a lower compression ratio of reconstructed image. This paper aims to present a method to improve performance of the full search algorithm by combining FIC (lossy compression) and another lossless technique (in this case entropy coding is used). The entropy technique will reduce size of the domain pool (i. e., number of domain blocks) based on the entropy value of each range block and domain block and then comparing the results of full search algorithm and proposed algorithm based on entropy technique to see each of which give best results (such as reduced the encoding time with acceptable values in both compression quali-ty parameters which are C. R (Compression Ratio) and PSNR (Image Quality). The experimental results of the proposed algorithm proven that using the proposed entropy technique reduces the encoding time while keeping compression rates and reconstruction image quality good as soon as possible.

References

JyotiBholaand SimarpreetKaur, ”Encoding Time Reduction Method For The Wavelet Based Fractal Image Compression”, Inter-national Journal of Computer Engineering Science (IJCES),Vol.2, Issue 5, May 2012.

S. Michael Vanitha and K. Kuppusamy, ”Survey On Fractal Image Compression”, International Journal of Computer Trends and Technology (IJCTT), Vol.4, May 2013.

Yih-Lonlin and Wen-Linchen, "Fast Search Strategies For Fractal Image Compression", Department of Information Engineering ,I-Shou University Kaohsiung, 840 Taiwan, Journal Of Information Science and Engi-neering 28, 2012.

Mahdi Jampour, Mahdi Yaghoobi and Mar-yam Ashourzadeh, ”Fractal Images Com-pressing By Estimating The Closest Neigh-borhood With Using Of Schema Theory”, Journal of Computer Science 6 (5): 591-596, 2010.

K. Revathy and M. Jayamohan, ”Dynamic Domain Classification For Fractal Image Compression”, International Journal of Computer Science & Information Technolo-gy (IJCSIT), Vol.4, No 2, April 2012.

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Published

2018-04-11

How to Cite

[1]
D. Y. Abbaas, “Fractal Image Compression Based on High Entropy Values Technique”, MJS, vol. 28, no. 2, pp. 119–133, Apr. 2018.

Issue

Section

Computer Science