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, Accepted: 18 September 2022, Published Online: 27 September 2022
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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.
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