Image Compression-Based Discrete Wavelet Transform Combined with Zooming-out Algorithm
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Abstract
Image compression is one of the most important processes in image processing. It has numerous applications and plays a significant role in increasing transmission efficiency by reducing image size to just a few bytes, enabling the transmission of images without compromising their quality. The present study's proposed method focuses on reducing the size of an image through a compression technique. This technique involves extracting the essential information from the original image, which is represented by the low-frequency coefficients obtained through the discrete wavelet transform (DWT). By discarding the high-frequency coefficients, it can effectively preserve only the important details while significantly reducing the image size. In our approach, we employ the bi-cubic zoom-out method to resize the low-frequency coefficients, resulting in a high compression ratio. Finally, these high-frequency data are reduced by the Minimize-Final-Data method to produce compressed data (a stream of bits). Importantly, this zoomed-out image does not affect the overall image quality. We compared this proposed algorithm with both JPEG and JPEG2000 based on image quality and compression ratio. When applied to the Lena image, the proposed method achieved a PSNR of 40 dB and a compression size of 13.8 KB. However, when compared to the JPEG2000 method, the PSNR increased to 41.1 dB while maintaining a smaller compression size of 13.3 KB.
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References
Yunis AA, Abdurrahman EH. Comparison Among Some Image Zooming Methods. College of Basic Education Researchers Journal 2013; 12(3):761-774.
Soudani RJ. Supporting Zooming-in Process for Image Compression Based on High-Order Weighted 3D Polynomials Fitting. Iraqi Journal of Computers, Communications, Control & Systems Engineering (IJCCCE) 2012; 16(1):29-37.
Siddeq MM. JPEG and Sequential Search Algorithm Applied on Low-Frequency Sub-Band for Image Compression (JSS). Journal of Information and Computing Science 2010; 5(3):163-172.
Zhao W, Zhao M, Pan J. The Image Compression Technology Based on Wavelet Transform. Advanced Materials Research 2015; 1078:370-374.
Sekar K, Duraisamy V, Remimol AM. An Approach of Image Scaling Using DWT and Bicubic Interpolation. International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE), Coimbatore, India, 2014; 1-5.
Jeromel A, Žalik B. An Efficient Lossy Cartoon Image Compression Method. Multimedia Tools and Applications 2020; 79(1):433-451.
Hamoodat H, Alazzawi NM, Abduljabber RQ, Siddeq MM. Image Compression Based on Frequency Domain Reduction Size. 1st International Conference on Sustainable Development Techniques (ICSDT2022), Nineveh, Iraq, 2022; 1-7.
Salih OM, Rostum HM, Vásárhelyi J, Siddeq MM. Fast Joint Image Compression-Encryption Algorithm Used for 3D Reconstruction. 24th IEEE International Carpathian Control Conference (ICCC), Szilvásvárad, Hungary, 2023; 400-405.
Siddeq MM, Rasheed MH, Salih OM. Quick Sequential Search Algorithm Used to Decode High-Frequency Matrices. Electrical and Computer Engineering 2023; 17(8):180-187.
Abdullah D, Fajriana F, Maryana M, Rosnita L, Utama S, Andysah P, Rahim R, Harliana P, Harmayani H, Ginting Z, Erliana C, Irwansyah D, Zulmiardi Z, Khaddafi M, Milanie F, Aspan H, Huda I, Kundharu S, Mulyaningsih I. Application of Interpolation Image by Using Bi-Cubic Algorithm. Journal of Physics: Conference Series 2018; 012066.
Jia Z, Huang Q. Image Interpolation with Regional Gradient Estimation. Applied Sciences 2022; 12(15):1-15.