Development of Hybrid Based Lossless Iris Image Compression Technique
DOI:
https://doi.org/10.31695/IJERAT.2022.8.7.2Keywords:
Iris image compression, Zipper transform, Image processing, Polynomial coding, Hybrid codingAbstract
The biometric iris recognition is the most popular method and is extended successfully in use, but unfortunately, a large number of data bits are exhausted when stored that implicitly affecting the transmission bandwidth and a compression solution is urgently required based on removing redundancy(s).
In this paper a simple hybrid image compression scheme is proposed, it is based on using two techniques of polynomial coding and Fourier transform coding to efficiently encoded lossless. The test results of the suggested method showed that improved of compression performance is achieved with an identically based reconstruction image.
Key Words: Iris image compression, Zipper transform, Image processing, Polynomial coding, and Hybrid coding.
References
Gonzalez, R. C.and R. E. Woods, 2002.“Digital Image Processing”, 2nd edition, Prentice Hall, New Jersey.
. Amruta, S.G. and Sanjay L.N, “A Review on Lossy to Lossless Image Coding”. International Journal of Computer Applications (IJCA),Vol. 67,No.17,pp. 9-16.,2013
Rasha, T. 2015. ” Image Compression Using Enhancement Polynomial Prediction Coding”, M.Sc. Work, University of Baghdad.
Marimuthu, M. and Swaminathan, P. Review Article:” An Overview of Image Compression Techniques”. Research Journal of Applied Science, Engineering and Technology, Vol .24,No.4,pp. 5381 -5386,2014
Sachin, D.” A Review of Image Compression and Comparison of its Algorithms”. International Journal of Electronics & Communication Technology, Vol.2,No.1,pp22-26 , 2011.
Khobragede, P. and Thakare, S. Image Comprssion Techniques-A Review International Journal of Computer Science and Information Technologies, Vol.5,No.1,pp. 272-275,2014
Ghadah Al-Kafagi, Hazeem Al-K.,"Medical Image Compression usingWavelet Quadrants of Polynomial Prediction Coding & Bit Plane Slicing,"Medical Image Compression, vol.4, no.6,
Ghadah, Al-K. 2012. Intra and Inter Frame Compression for Video Streaming. Ph.D. thesis, Exeter University, UK.
Noor, A. 2018. Experimental Study of Compression Techniques of Fourier Transform for Biometric Iris Data, Higher Diploma dissertation, University of Baghdad, College of Science.
Shaymaa F., 2017. "The Use of Haar Wavelet and Polynomial Coding for Compressing Grayscale Image", Iraq: Higher diploma dissertation, Departement of Computer Science, University of Baghdad, Collage of Science.
Babatunde S., 2015. "Development of an Improved Approach to Biometric Fingerprint Image Compression using Coiflet Signal Transformation Algorithm", Nigeria: Ph.D. thesis. Ahmed Bello University.
GEORGE, L. E. AND GHADAH, AL-K. 2013. “Fast Lossless Compression of Medical Images based on Polynomial”. International Journal of Computer Applications, 70(15), 0975-8887.
GEORGE, L. E. AND DHANNON, B. N. 2013.“Image Compression Using Polynomial and Quadtree Coding Techniques”.International Journal of Scientific & Engineering Research, 4(11), 2229-5518.
GHADAH, AL-K. 2013. “Hybird Image Compression based on Polynomial and Block Truncation Coding”. Electrical Communication Computer, Power and Control Engineering (ICECCPCE), International Conference on Mosul, IEEE,179-184.
ABDULLAH, A. 2018. Hierarchal Polynomial Coding for Grayscale Lossless Image Compression. Diploma dissertation, University of Baghdad, Collage of Science.
BABAJIDE, O. 2017. A Fast and Efficient Near-Lossless Image Compression Using Zipper Transformation, Electrical and Computer Engineering University of Louisville, Louisville, KY 40218, 1 - 13.
SANGEETHA, M., BETTY, P. AND NANDA, K. 2017. A Biometrie Iris Image Compression Using LZW and Hybrid LZW Coding Algorithm. International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), Coimbatore, India, IEEE Xplore.
KHOBRAGEDE, P. AND THAKARE, S.2014. Image Comprssion Techniques-A Review International Journal of Computer Science and Information Technologies, 5(1), 272-275.
BENS, P., AND INGRID, C. 2012. Comparative Evaluation on Iris Recognition Performance. International Journal Of Mathematical Models And Methods In Applied Sciences, 6(2), 332 - 339.
JOHN, D. AND CATHRYN, D. 2008. Effect of Severe Image Compression on Iris Recognition Performance. IEEE Transactions On Information Forensics And Security, 3(1), 52 - 61.
ROBERT, W., YINGZI, D. AND DANIEL, A. 2009. Effects of Image Compression on Iris Recognition Performance and Image Quality, Conference: Computational Intelligence in Biometrics: Theory, Algorithms, and Applications, IEEE Xplore, CIB.2009.4925681, 2-7.
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Copyright (c) 2022 Rasha Talib Gdeeb, Ghadah Al-Khafaji, Noor Ali J
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