Images Compression using Combined Scheme of Transform Coding
by Zainab Jawad Ahmed 1,* 
, Loay Edwar George 2 , Raad Ahmed Hadi 3
1 Department of Biology Science, College of Science, University of Baghdad, Baghdad, 10011, Iraq
2 University of Information Technology and Communications, Baghdad, 10011, Iraq
3 Al-Iraqia University, Baghdad, 10011, Iraq
* Author to whom correspondence should be addressed.
Journal of Engineering Research and Sciences, Volume 1, Issue 9, Page # 8-14, 2022; DOI: 10.55708/js0109002
Keywords: Wavelet Decomposition, DCT De-correlation, Scalar Quantization, String Table Encoding
Received: 11 August 2022, Revised: 17 September 2022, Accepted: 18 September 2022, Published Online: 27 September 2022
APA Style
Ahmed, Z. J., George, L. E., & Hadi, R. A. (2022). Images Compression using Combined Scheme of Transform Coding. Journal of Engineering Research and Sciences, 1(9), 8–14. https://doi.org/10.55708/js0109002
Chicago/Turabian Style
Ahmed, Zainab Jawad, Loay Edwar George, and Raad Ahmed Hadi. “Images Compression using Combined Scheme of Transform Coding.” Journal of Engineering Research and Sciences 1, no. 9 (September 1, 2022): 8–14. https://doi.org/10.55708/js0109002.
IEEE Style
Z. J. Ahmed, L. E. George, and R. A. Hadi, “Images Compression using Combined Scheme of Transform Coding,” Journal of Engineering Research and Sciences, vol. 1, no. 9, pp. 8–14, Sep. 2022, doi: 10.55708/js0109002.
Some problems want to be solved in image compression to make the process workable and more efficient. Much work had been done in the field of lossy image compression based on wavelet and Discrete Cosine Transform (DCT). In this paper, an efficient image compression scheme is proposed, based on a common encoding transform scheme; It consists of the following steps: 1) bi-orthogonal (tab 9/7) wavelet transform to split the image data into sub-bands, 2) DCT to de-correlate the data, 3) the combined transform stage’s output is subjected to scalar quantization before being mapped to positive, 4) and LZW encoding to produce the compressed data. The peak signal-to-noise (PSNR), compression ratio (CR), and compression gain (CG) measures were used to perform a comparative analysis of the performance of the whole system. Several image test samples were used to test the performance behavior. The simulation results show the efficiency of these combined transformations when LZW is used in the field of data compression. Compression outcomes are encouraging and display a significant reduction in image file size at good resolution.
- G. Xin, P. Fan, “A lossless compression method for multi‑component medical images based on big data mining,” Scientific Reports, vol.11, no.12372, pp.1-11, June 2021, DOI: 10.1038/s41598-021-91920-x.
- N. Lata, “Image Compressions Techniques: A Review,” Journal of Emerging Technologies and Innovative Research (JETIR), vol.6, no.3, pp. 368-375, March 2019.
- S. Mathew, S. Sebastian, “Comparison of Jpeg Compression Technique with Shape Adaptive DCT Technique (SA-DCT),” International Journal of Advance Research in Computer Science and Management Studies, vol. 3, no.4, pp.412-417, April 2015.
- T. Acharya, P.-S. Tsai, JPEG2000 Standard for Image Compression: Concepts, Algorithms and VLSI Architectures, Wiley, October 2004.
- S.S.Pandey et al., “Block wise image compression and block reduced artifacts using discrete cosine transform,” International Journal of Scientific and Research Publications, vol.5, no.3, pp.1-10, March 2015.
- W.M. Abd-Elhafiez, E. O. Abdel-Rahman, “New Efficient Method for Coding Color Images,” Applied Mathematics and Information Sciences an International Journal, vol.10, no.1, pp.357–361, January 2016, doi: 10.18576/amis/100138.
- W. Xiao et al., “A Fast JPEG Image Compression Algorithm Based on DCT,” IEEE International Conference on Smart Cloud, USA, pp.106-110, November 2020, doi: 10.1109/SmartCloud49737.2020.00028.
- S. L. Agrwal et al. , “Improved image compression technique using IWT-DCT transformation,” 2nd International Conference on Next Generation Computing Technologies (NGCT), IEEE, Dehradun, pp. 683–686, October 2016, doi: 10.1109/NGCT.2016.7877499.
- R. K. Gaber et al., “Image Compression Using High Level Wavelet Transformer with Non-Uniform Quantizer and Different Levels Huffman Codes,” IOP Conf. Series: Materials Science and Engineering, vol. 765, no.1:012072, pp.1-13, March 2020, doi:10.1088/1757-899X/765/1/012072.
- H. Kanagaraj, V. Muneeswaran, “Image compression using HAAR discrete wavelet transform,” 5th International Conference on Devices, Circuits and Systems (ICDCS), IEEE, India, March 2020, doi: 10.1109/ICDCS48716.2020.243596.
- N. Ahmed et al., “Discrete cosine transform,” IEEE transactions on Computers, vol. C-23, no.1, pp. 90-93, 1974, doi: 10.1109/T-C.1974.223784.
- F. Alfiah et al., “Discrete Cosine Transform DCT Methods on Compression RGB and Grayscale image,” International Journal of Computer Techniques, vol. 4, no.60, pp. 24, 29, December, 2017.
- S. V Konlade, V.S Gangwani, “Color Image Compression Using DCT & DWT,” International Journal of Scientific Development and Research (IJSDR) , vol. 2, no.6, pp. 258-263, June 2017.
- A. Kurniawa et al., “Implementation of Image Compression Using Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT),” International Journal of Applied Engineering Research, vol. 12, no.23, pp.13951-13958, 2017.
- Z. J. Ahmed, Loay E. George, “A Comparative Study Using LZW with Wavelet or DCT for Compressing Color Images,” Third International Conference on Advanced Science and Engineering (ICOASE2020), Iraq, pp. 53-58, 2020, doi: 10.1109/ICOASE51841.2020.9436622.
- Z. J. Ahmed, L. E. George, “Lightweight Image Compression Using Polynomial and Transform Coding,” V. International Scientific Congress of Pure, Applied and Technological Sciences (Minar Congress), Rimar Academy Turkey, pp.172-192, 2022.
- P.Ravi, A.Ashokkumar, “A Study of Various Data Compression Techniques,” International Journal of Computer Science and Communication, vol. 6, no.2, pp. 1-8, 2015.
- E. Kh. Hassan et al., “Color Image Compression Based on DCT, Differential Pulse Coding Modulation, and Adaptive Shift Coding,” Journal of Theoretical and Applied Information Technology, vol.96, no.11, pp. 3160-3171, June 2018.
- A.A. Hussain et al., “Developed JPEG Algorithm Applied in Image Compression,” 2nd International Scientific Conference of Al-Ayen University (ISCAU-2020) IOP Publishing, pp.1-17, November 2020, doi: 10.1088/1757-899X/928/3/032006.
- Sh. Othman et al., “Lossy Compression using Adaptive Polynomial Image Encoding,” Advances in Electrical and Computer Engineering, vol. 21, no.1, pp.91-98, February 2021, doi: 10.4316/AECE.2021.01010.
