Comparison of SVD and FFT in Image Compression

Two image compression methods are compared: Singular Value Decomposition (SVD) and Fast Fourier Transform (FFT). SVD is the factorization of a real or complex matrix, while FFT is an algorithm which allows low pass and high pass filtering with a great degree of accuracy. FFT is also a process that vastly reduces the time needed to compute large matrices. Distortion and compression ratios for each method were calculated at different parameters. Images were compressed without sacrificing significant image quality. Comparing the compression ratio, distortion, and visual quality of the images, FFT was determined to be the better of the two compression methods.