NEWS

A COMPARATIVE STUDY OF DCT AND DWT IMAGE COMPRESSION TECHNIQUES COMBINED WITH HUFFMAN CODING

(Received: 2019-04-11, Revised: 2019-05-27 , Accepted: 2019-06-09)
Ashraf Maghari,
Image compression techniques have been widely used to store and transmit data which requires storage space and high transfer speed. The explosive growth of high-quality photos leads to the requirement of efficient technique to store and exchange data over the internet. In this paper, we present a comparative study to compare between the Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) algorithms in combination with Huffman algorithm; DCT-H and DWT-H. The comparison is based on five factors: Compression Ratio (CR), Mean Square Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM) and compression/decompression time. The experiments are conducted on five BMP gray- scale file images. We found out that DWT-H coding is comparable to DCT-H coding in term of CR and outperforms DCT-H in terms of MSE, PSNR and SSIM. The CR average results of the five test images for DCT- H and DWT-H are 2.36 and 3.17, respectively. Moreover, DCT-H has the average results of MSE = 13.19, PSNR = 37.15 and SSIM = 0.76, while WDT-H has the average results of MSE = 4.54, PSNR = 42.5 and SSIM = 0.85. On the other hand, DCT-H outperforms DWT-H in term of execution time for compression and decompression. DCT-H has an average compression time of 0.358s and an average decompression time of 0.122s, while WDT-H has 2.38s compression time and 2.13s decompression time.
  1. M. Gupta and A. K. Garg, "Analysis of Image Compression Algorithm Using DCT," Int. J. Eng. Res. Appl., vol. 2, no. 1, pp. 515–521, 2012.
  2. T. Sheltami, M. Musaddiq and E. Shakshuki, "Data Compression Techniques in Wireless Sensor Networks," Futur. Gener. Comput. Syst., vol. 64, pp. 151–162, 2016.
  3. J. Uthayakumar, T. Vengattaraman and P. Dhavachelvan, "A Survey on Data Compression Techniques: From the Perspective of Data Quality, Coding Schemes, Data Type and Applications," J. King Saud Univ. Inf. Sci., 2018.
  4. A. Mr, S. Subramanian and S. Mr, "Comparing PSNR of Different Image Transforms (DCT, DFT, DWT, DHT, DTT)," 2018.
  5. Nashar Luthfi Sugara, T. W. Purboyo and A. L. Prasasti, "Comparative Analysis of Image Compression Using Huffman and DCT Method on JPG Image," J. Eng. Appl. Sci., vol. 13, pp. 4447-4452, 2018.
  6. G. Suseela and Y. A. V. Phamila, "Energy Efficient Image Coding Techniques for Low Power Sensor Nodes: A Review," Ain Shams Eng. J., vol. 9, no. 4, pp. 2961–2972, 2018.
  7. W. Z. Wahba and A. Y. A. Maghari, "Lossless Image Compression Techniques: Comparative Study," Int. Res. J. Eng. Technol., vol. 3, no. 2, pp. 1–9, 2016.
  8. A. H. M. J. I. Barbhuiya, T. A. Laskar and K. Hemachandran, "An Approach for Color Image Compression of JPEG and PNG Images Using DCT and DWT," Proc. of the International Conference on Computational Intelligence and Communication Networks (CICN), pp. 129–133, 2014.
  9. N. Saroya and P. Kaur, "Analysis of Image Compression Algorithm Using DCT and DWT Transforms," Int. J. Adv. Res. Comput. Sci. Softw. Eng., vol. 4, no. 2, 2014.
  10. T. Kumar and R. Kumar, "Medical Image Compression Using Hybrid Techniques of DWT, DCT and Huffman Coding," Int. J. Innov. Res. Electr. Electron. Instrum. Control Eng., vol. 3, no. 2, pp. 54–60, 2015.
  11. G. K. Wallace, "The JPEG Still Picture Compression Standard," IEEE Trans. Consum. Electron., vol. 38, no. 1, pp. xviii–xxxiv, 1992.
  12. Ze-Nian Li, Drew, Jiangchuan Liu and Mark S. Drew, Fundamentals of Multimedia, 2nd Ed., Springer International Publishing, 2014.
  13. C. Wang, R. Xiong, H. He, Y. Zhang and W. Shen, "Comparison of Decomposition Levels for Wavelet Transform-based Energy Management in a Plug-in Hybrid Electric Vehicle," Journal of Cleaner Production, vol. 210, pp. 1085–1097, 2019.
  14. A. J. Maan, "Analysis and Comparison of Algorithms for Lossless Data Compression," Int. J. Inf. Comput. Technol., vol. 3, no. 3, pp. 139–146, 2013.
  15. A. M. G. Hnesh and H. Demirel, "DWT-DCT-SVD Based Hybrid Lossy Image Compression Technique," Proc. of the International Conference on Image Processing, Applications and Systems (IPAS), pp. 1–5, 2016.
  16. R. Kher and Y. Patel, "Medical Image Compression Framework Based on Compressive Sensing, DCT and DWT," Biology, Engineering and Medicine, vol. 2, no. 2, pp. 1–4, 2017.
  17. S. Sharma and S. Kaur, "Image Compression Using Hybrid of DWT, DCT and Huffman Coding," Int. J. Sci. Emerg. Technol. with Latest Trends, vol. 5, no. 1, pp. 19–23, 2013.
  18. O. Ghorbel, "DCT & DWT Image Compression Algorithms in Wireless Sensor Networks: Comparative Study and Performance Analysis," Int. J. Wirel. Mob. Networks, 2013.
  19. A. Katharotiya, S. Patel and M. Goyani, "Comparative Analysis between DCT and DWT Techniques of Image Compression," J. Inf. Eng. Appl., vol. 1, no. 2, pp. 9–17, 2011.
  20. R. Monika, S. Dhanalakshmi and S. Sreejith, "Coefficient Random Permutation Based Compressed Sensing for Medical Image Compression," Advances in Electronics, Communication and Computing, Springer, pp. 529–536, 2018.
  21. Z. Wang, A. C. Bovik, H. R. Sheikh, E. P. Simoncelli et al., "Image Quality Assessment: From Error Visibility to Structural Similarity," IEEE Transactions on Image Processing, vol. 13, no. 4, pp. 600–612, 2004. 86 "A Comparative Study of DCT and DWT Image Compression Techniques Combined with Huffman Coding , A. Maghari.
  22. R. Reisenhofer, S. Bosse, G. Kutyniok and T. Wiegand, "A Haar Wavelet-based Perceptual Similarity Index for Image Quality Assessment," Signal Processing: Image Communication, vol. 61, pp. 33–43, 2018.
  23. K. Ma, Z. Duanmu, H. Yeganeh and Z. Wang, "Multi-exposure Image Fusion by Optimizing a Structural Similarity Index," IEEE Transactions on Comput. Imaging, vol. 4, no. 1, pp. 60–72, 2018.
  24. S. Pistonesi, J. Martinez, S. M. Ojeda and R. Vallejos, "Structural Similarity Metrics for Quality Image Fusion Assessment: Algorithms," Image Process. Line, vol. 8, pp. 345–368, 2018.
  25. Z. Wang and A. C. Bovik, "Mean Squared Error: Love It or Leave It? A New Look at Signal Fidelity Measures," IEEE Signal Processing Magazine, vol. 26, no. 1, pp. 98–117, 2009.
  26. A. Hore and D. Ziou, "Image Quality Metrics: PSNR vs. SSIM," Proc. of the 20th International Conference on Pattern Recognition, pp. 2366–2369, 2010.