Image Compression Exploration using Discrete Wavelets Transform Families and Level

Arbab Waseem Abbas; Waseem Ullah Khan; Safdar Nawaz Khan Marwat; Salman Ahmed; Khalid Saeed; Noor ul Arfeen

Authors

Arbab Waseem Abbas
Waseem Ullah Khan Institute of Computer Science and Information Technology, Faculty of Management and Computer Sciences, The University of Agriculture, Peshawar, 25000, Pakistan.
Safdar Nawaz Khan Marwat Department of Computer Systems Engineering, Faculty of Electrical and Computer Engineering, University of Engineering and Technology, Peshawar, 25000, Pakistan.
Salman Ahmed 3Secured IoT Devices Lab, Department of Computer Systems Engineering, University of Engineering and Technology, Peshawar, 25000, Pakistan.
Khalid Saeed Department of Computer Science, Shaheed Benazir Bhutto University, Sheringal, Upper Dir, 18200, Pakistan.
Noor ul Arfeen Institute of Computer Science and Information Technology, Faculty of Management and Computer Sciences, The University of Agriculture, Peshawar, 25000, Pakistan.

Keywords:

Image Compression, Discrete Wavelets Transform (DWT), Wavelet Families, Decomposition Levels, Compression Ratio

Abstract

This analysis paper is based on Discrete Wavelets Transform (DWT) for image compression using wavelets families and levels. The DW transforms the image or data into frequency components that match its resolution scale while the compression removes duplication and unwanted information on the receiver side. Wavelets in compression observe the whole image very finely and thus produce no blocking artifacts. Thus, wavelets are high-quality image compression used in many real-world applications i.e. image, multimedia, biometric and biological analysis, computer graphics and image processing, etc. In this investigation, first of all, various compression methods have been compared. It is validated based on compression ratio that DWT is the optimal choice. Secondly, for experimental and analysis purposes, random real-time digital images both RGB and greyscale have been used as a dataset. The assessment images have been converted to grayscale if RGB, decomposed using wavelet levels, and compressed using wavelet families. Threshold coefficients have been evaluated by the Birge-Massart strategy using two scenarios i.e. simulator control thresholding and increasing threshold. Birge-Massart thresholding is best for the compression of still images in wavelet transform. e evaluation and comparison of various wavelet families and decomposition levels were conducted based on criteria such as image compression effectiveness, retained energy, and zero coefficients. The size of original, compressed, and decompressed images has also been computed and displayed for analysis purposes. The analysis of wavelet families and decomposition levels indicated that increasing levels up to a certain range for decomposition purposes in various wavelet compression families enhances image smoothness consistently. With image smoothness, roughness, and noise spikes in images have been reduced. However, it is observed that after specific levels, image quality degradation has been observed. The significance and novelty of the work provide analysis for appropriate and effective quality image compression using DWT families and levels in different applications. The purpose is to reduce need-based storage requirements and lightweight transmission. Additionally, the optimum compression algorithm in DWT families and levels is also found based on the results. As selection of wavelet filters and decomposition level play an important role in achieving an effective compression performance because no filter performs the best for all images.

References

V. A. Coutinho, R. J. Cintra, F. M. Bayer, P. A. M. Oliveira, R. S. Oliveira, and A. Madanayake, “Pruned Discrete Tchebichef Transform Approximation for Image Compression,” Circuits, Syst. Signal Process., vol. 37, no. 10, pp. 4363–4383, Oct. 2018, doi: 10.1007/S00034-018-0768-X/METRICS.

A. Sarwar, A. M. Alnajim, S. N. K. Marwat, S. Ahmed, S. Alyahya, and W. U. Khan, “Enhanced Anomaly Detection System for IoT Based on Improved Dynamic SBPSO,” Sensors 2022, Vol. 22, Page 4926, vol. 22, no. 13, p. 4926, Jun. 2022, doi: 10.3390/S22134926.

S. Alyahya, W. U. Khan, S. Ahmed, S. N. K. Marwat, and S. Habib, “Cyber Secure Framework for Smart Agriculture: Robust and Tamper-Resistant Authentication Scheme for IoT Devices,” Electron. 2022, Vol. 11, Page 963, vol. 11, no. 6, p. 963, Mar. 2022, doi: 10.3390/ELECTRONICS11060963.

I. Daubechies, M. Barlaud, and P. Mathieu, “Image Coding Using Wavelet Transform,” IEEE Trans. Image Process., vol. 1, no. 2, pp. 205–220, 1992, doi: 10.1109/83.136597.

A. Joseph, O. Okassa, J. P. Ngantcha, A. Ndtoungou, and P. Ele, “Use of Lazy Wavelet and DCT for Vibration Signal Compression,” Am. J. Eng. Appl. Sci., vol. 14, no. 1, pp. 1–6, Jan. 2021, doi: 10.3844/AJEASSP.2021.1.6.

R. Ranjan, “Canonical Huffman Coding Based Image Compression using Wavelet,” Wirel. Pers. Commun., vol. 117, no. 3, pp. 2193–2206, Apr. 2021, doi: 10.1007/S11277-020-07967-Y/METRICS.

R. Starosolski, “Employing New Hybrid Adaptive Wavelet-Based Transform and Histogram Packing to Improve JP3D Compression of Volumetric Medical Images,” Entropy 2020, Vol. 22, Page 1385, vol. 22, no. 12, p. 1385, Dec. 2020, doi: 10.3390/E22121385.

M. A. Kabir and M. R. H. Mondal, “Edge-based and prediction-based transformations for lossless image compression,” J. Imaging, vol. 4, no. 5, 2018, doi: 10.3390/JIMAGING4050064.

M. A. Rahman, M. Hamada, and J. Shin, “The Impact of State-of-the-Art Techniques for Lossless Still Image Compression,” Electron. 2021, Vol. 10, Page 360, vol. 10, no. 3, p. 360, Feb. 2021, doi: 10.3390/ELECTRONICS10030360.

Z. Yin, W. Z. Shi, Z. Wu, and J. Zhang, “Multilevel wavelet-based hierarchical networks for image compressed sensing,” Pattern Recognit., vol. 129, p. 108758, Sep. 2022, doi: 10.1016/J.PATCOG.2022.108758.

S. Khan, S. Nazir, A. Hussain, A. Ali, and A. Ullah, “An efficient JPEG image compression based on Haar wavelet transform, discrete cosine transform, and run length encoding techniques for advanced manufacturing processes,” Meas. Control (United Kingdom), vol. 52, no. 9–10, pp. 1532–1544, Nov. 2019, doi: 10.1177/0020294019877508/ASSET/IMAGES/LARGE/10.1177_0020294019877508-FIG13.JPEG.

W. U. Khan, S. N. K. Marwat, and S. Ahmed, “Cyber Secure Framework for Smart Containers Based on Novel Hybrid DTLS Protocol,” Comput. Syst. Sci. Eng., vol. 43, no. 3, pp. 1297–1313, May 2022, doi: 10.32604/CSSE.2022.024018.

A. Kourav and A. Sharma, “Comparative analysis of wavelet transform algorithms for image compression,” Int. Conf. Commun. Signal Process. ICCSP 2014 - Proc., pp. 414–418, Nov. 2014, doi: 10.1109/ICCSP.2014.6949874.

A. Sarwar, S. Hasan, W. U. Khan, S. Ahmed, and S. N. K. Marwat, “Design of an Advance Intrusion Detection System for IoT Networks,” 2nd IEEE Int. Conf. Artif. Intell. ICAI 2022, pp. 46–51, 2022, doi: 10.1109/ICAI55435.2022.9773747.

C. Z. Basha, K. M. Sricharan, C. K. Dheeraj, and R. Ramya Sri, “A Study on Wavelet Transform Using Image Analysis,” Int. J. Eng. Technol., vol. 7, no. 2.32, pp. 94–96, May 2018, doi: 10.14419/IJET.V7I2.32.13535.

A. Maghari, “A comparative study of DCT and DWT image compression techniques combined with Huffman coding,” Jordanian J. Comput. Inf. Technol., vol. 5, no. 2, pp. 73–86, Aug. 2019, doi: 10.5455/JJCIT.71-1554982934.

D. Mody, P. Prajapati, P. Thaker, and N. Shah, “Image Compression Using DWT and Optimization Using Evolutionary Algorithm,” SSRN Electron. J., Apr. 2020, doi: 10.2139/SSRN.3568590.

S. P. Nanavati and P. K. Panigrahi, “Wavelets: Applications to image compression-I,” Reson. 2005 102, vol. 10, no. 2, pp. 52–61, Feb. 2005, doi: 10.1007/BF02835922.

S. Sidhik, “Comparative study of Birge–Massart strategy and unimodal thresholding for image compression using wavelet transform,” Optik (Stuttg)., vol. 126, no. 24, pp. 5952–5955, Dec. 2015, doi: 10.1016/J.IJLEO.2015.08.127.

M. Rabbani and R. Joshi, “An overview of the JPEG 2000 still image compression standard,” Signal Process. Image Commun., vol. 17, no. 1, pp. 3–48, Jan. 2002, doi: 10.1016/S0923-5965(01)00024-8.

M. A. Rahman and M. Hamada, “Lossless Image Compression Techniques: A State-of-the-Art Survey,” Symmetry 2019, Vol. 11, Page 1274, vol. 11, no. 10, p. 1274, Oct. 2019, doi: 10.3390/SYM11101274.

J. A. Aghamaleki and A. Ghorbani, “Image fusion using dual tree discrete wavelet transform and weights optimization,” Vis. Comput., vol. 39, no. 3, pp. 1181–1191, Mar. 2023, doi: 10.1007/S00371-021-02396-9/METRICS.

J. Ma, J. Jiang, C. Liu, and Y. Li, “Feature guided Gaussian mixture model with semi-supervised EM and local geometric constraint for retinal image registration,” Inf. Sci. (Ny)., vol. 417, pp. 128–142, Nov. 2017, doi: 10.1016/J.INS.2017.07.010.

M. C. Yesilli, J. Chen, F. A. Khasawneh, and Y. Guo, “Automated surface texture analysis via Discrete Cosine Transform and Discrete Wavelet Transform,” Precis. Eng., vol. 77, pp. 141–152, Sep. 2022, doi: 10.1016/J.PRECISIONENG.2022.05.006.