Comparative Performance Evaluation of Three Image Compression Algorithms

Authors

  • Jide Julius Popoola Department of Electrical and Electronics Engineering, School of Engineering and Engineering Technology, Federal University of Technology, Akure, Nigeria
  • Michael Elijah Adekanye Department of Electrical and Electronics Engineering, School of Engineering and Engineering Technology, Federal University of Technology, Akure, Nigeria

DOI:

https://doi.org/10.33736/jaspe.371.2017

Keywords:

Image compression, image, image compression methods, performance indices.

Abstract

The advent of computer and internet has brought about massive change to the ways images are being managed. This revolution has resulted in changes in image processing and management as well as the huge space requirement for images’ uploading, downloading, transferring and storing nowadays. In guiding against this huge space requirement, images need to be compressed before either storing or transmitting. Several algorithms or techniques on image compression had been developed in literature. In this study, three of these image compression algorithms were developed using MATLAB codes. The three algorithms developed are discrete cosine transform (DCT), discrete wavelet transform (DWT) and set partitioning in hierarchical tree (SPIHT). In order to ascertain which of them is most appropriate for image storing and transmission, comparative performance evaluations were conducted on the three developed algorithms using five performance indices. The results of the comparative performance evaluations show that the three algorithms are effective in image compression but with different efficiency rates. In addition, the comparative performance evaluations results show that DWT has the highest compression ratio and distortion level while the corresponding values for SPIHT is the lowest with those of DCT fall in-between. Also, the results of the study show that the lower the mean square error and the higher the peak signal-to-noise-ratio, the lower the distortion level in the compressed image.

References

Singh, Singh, A.K. and Tripathi, G.S. (2014). A Comparative Study of DCT, DWR and hybrid (DCTDWT) Transform, GJESER Review Paper, Vol. 1, No. 4, pp. 16-21.

Dhawan, S. (2011). A Review of Image Compression and Comparison of its Algorithms, International Journal of Electronics and Communication Technology, Vol. 2, No. 1, pp. 22-26.

Gupta, B. (2013). Study of Various Lossless Image Compression Techniques, International Journal of Emergimg Trends and Technology in Computer Science, Vol. 2, No. 4, pp. 253-257.

Rehman, M., Sharif, M. and Raza, M. (2014). Image Compression: A Survey, Research Journal of Applied Sciences, Engineering and Technology, Vol. 7, No. 4, pp. 256-672.

https://doi.org/10.19026/rjaset.7.303

Nivedita, M. Singh, P., and Jindal, S. (2012). A Comparative Study of DCT And DWT-SPIHT. International Journal of Computational Engineering and Management, Vol. 15, No. 2, pp. 26-32.

Pensiri, F. and Auwatanamongkol, S. (2012). A Lossless Image Compression Algorithm Using Predictive Coding Based on Quantized Colors, WSEAS Transactions on Signal Processing, Vol. 8, No. 2, pp. 43-53.

Alarabeyyat, A., Al-Hashemi S., Khdour, T., Btoush, M.H., Bani-Ahmad, S. and Al-Hashemi, R. (2012), Lossless Image Compression Technique Using Combination Methods, Journal of Software Engineering and Applications, Vol. 5, pp. 752-763.

https://doi.org/10.4236/jsea.2012.510088

Weinberger, M.J., Seroussi, G. and Sapiro, G. (2000). The LOCO-I Lossless Image Compression Algorithm: Principles and Standardization into JPEG-LS, IEEE Trans. on Image Processing, Vol. 9, No. 8, pp. 1309-1324.

https://doi.org/10.1109/83.855427

Zuo, Z., Lan, X., Deng, L., Yao, S. and Wang, X. (2015). An Improved Medical Image Compression Technique with Lossless Region of Interest. Optik-International Journal for Light and Electron Optics, Vol. 126, No. 21, pp. 2825-2831.

https://doi.org/10.1016/j.ijleo.2015.07.005

Tomar, R.R.S. and Jain, K. (2016). Lossless Image Compression Using Differential Pulse Code Modulation and its Application, International Journal of Signal Processing, Image Processing and Pattern Recognition, Vol. 9, No. 1, pp. 197-202.

https://doi.org/10.14257/ijsip.2016.9.1.18

Masood, S., Sherif, M., Yasmin, M., Raza, M. and Mohsin, S. (2012). Brain Image Compression: A Brief Survey. Research Journal of Applied Sciences, Engineering and Technology, Vol. 5, No. 1, pp. 49-59.

https://doi.org/10.19026/rjaset.5.5083

Marimuthu, M., Muthaiah, R. and Swaminathan, P. (2012). Review Article: An Overview of Image Compression Techniques. Research Journal of Applied Sciences, Engineering and Technology, Vol. 4, No. 24, pp. 5381-5386.

Vijayvargiya, G., Silakari, S. and Pandy, R. (2013). A Survey: Various Techniques of Image Compression. International Journal of Computer and Information Security, Vol. 11, No. 10, pp. 51-55.

Rehna, V.J.and Jeya Kumar, M.K. (2012). Wavelet Based Image Coding Schemes: A Recent Survey, International Journal of Soft Computing, Vol. 3, No. 3, pp. 101-118.

https://doi.org/10.5121/ijsc.2012.3308

Wallace, G.K. (1991). The JPEG Still Picture Compression Standard, Communications of the ACM Magazine, Vol. 34, No. 4, pp. 30-44.

https://doi.org/10.1145/103085.103089

Puri, A. (1992). Video Coding Using the MPEG-1 Compression Standard, Society for Information Display Digest of Technical Papers, Vol. 23, pp. 123-126.

Watson, A.B. (1994). Image Compression Using the Discrete Cosine Transform, Mathematica Journal, Vol. 4, No. 1, pp. 81-88.

Yadavi, R.J., Gangwar, S.P. and Singh, H.V. (2012). Study and Analysis of Wavelet Based Image Compression Techniques International Journal of Engineering, Science and Technology, Vol. 4, No. 1, pp. 1-7.

https://doi.org/10.4314/ijest.v4i1.1S

Bindu, K., Ganpati, A. and Sharma, A.K. (2012). A Comparative Study of Image Compression Algorithms, International Journal of Research in Computer Science, Vol. 2, No. 5, pp. 37-42.

https://doi.org/10.7815/ijorcs.25.2012.046

Deshlahral, A., Shirnewar, G.S. and Sahoo, A.K. (2013). A Comparative Study of DCT, DWT and Hybrid (DCT-DWT) Transform, In Proceedings International Conference on Emerging Trends in Computer and Image Processing, February 24, pp. 1-7.

Al-Janabi, A.K. (2013). Low Memory Set-Partitioning in Hierarchical Trees Image Compression Algorithm, International Journal of Video and Image Processing and Network Security, Vol. 13, No. 2, pp. 12-18.

Rema, N.R., Binu A.O. and Mythili, P. (2015). Image Compression Using SPIHT with Modified Spatial Orientation Tress, Procedia Computer Science, Vol. 46, pp. 1732-1738.

https://doi.org/10.1016/j.procs.2015.02.121

Chowdhury, M.M.H. and Khatun, A. (2012). Image Compression Using Discrete Wavelet Transform, International Journal of Computer Science Issues, Vol. 9, No. 1, pp. 327-330.

Saffor, A., Ramli, A R. and Ng, K-H. (2001). A Comparative Study of Image Compression between JPEG and WAVELET, Malaysian Journal of Computer Science, Vol. 14, No. 1, pp. 39-45.

Downloads

Published

2017-04-28

How to Cite

Popoola, J. J., & Adekanye, M. E. (2017). Comparative Performance Evaluation of Three Image Compression Algorithms. Journal of Applied Science &Amp; Process Engineering, 4(1), 113–126. https://doi.org/10.33736/jaspe.371.2017