The Application of Dicrete Wavelet Transform for Digital Image Compression - Ahmad Khairul Umam; Pukky Tetralian Bantining Ngastiti; Aris Alfan; Zaqiyatus Shahadah; Amanda Fatma Muamalah (Jurnal Matematika Sains dan Teknologi, 2024)
This article explains Discrete Wavelet Transform (DWT) in image compression. Wavelet transform is a generalization of Fourier transform, consisting of discrete and continuous wavelet transform. DWT has many uses, including image compression, fingerprint recognition, and image denoising. This research aims to know the steps of digital image compression using DWT and compare the original and resulting images. Efforts of DWT in digital image compression go by DWT's process, determining the threshold, sorting the absolute value of the image whether it is minor or more significant (equal to) threshold value, then is processed, Inverse Discrete Wavelet Transform (IDWT). This research explains the Peak Signal-to-Noise Ratio (PSNR), computing time, and compression ratio for three examples: the image of the cameraman, Lena, and a cat. The results determine that the highest PSNR values are wavelet of coiflets 3 for the cameraman, biorthogonal 3.5 for Lena, and coiflets 3 for the cat. The fastest computation times are wavelet of symlets 4 for the cameraman, symlets 4, coiflets 3 for Lena, and Daubechies 4 for the cat. Then, the highest compression ratios are wavelet of symlets 4, biorthogonal 3.5, coiflets 3 for the cameraman, Haar for Lena, and symlets 4, biorthogonal 3.5 for the cat. The results of this research are we get steps of the discrete wavelet transform for digital image compression. Also, we obtain types of wavelets with the highest PSNR values, the fastest computation times, and the highest compression ratios.