COMPARISON OF SVD AND FFT IN IMAGE COMPRESSION 1 Vinita Cheepurupalli, 2 Sierra Tubbs, 3 Khadijah Boykin, 4 Dr. Naima Naheed 1 Spring Valley High School, 120 Sparkleberry Ln, Columbia, SC 29229 2 Biology, Chemistry, and Environmental Health Science Department 3 Physics and Engineering Department 4 Math and Computer Science Department Benedict College, 1600 Harden Street, Columbia SC 29204 Email addresses for 4 persons mentioned above: cheepurupalli@yahoo.com sierratubbs424@gmail.com kboykin13@sljhs.org naheedn@benedict.edu keywords: Image, SVD, FFT, Compression Ratio, Distortion Abstract: 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. Contact Person: Dr. Naima Naheed Email: naheedn@benedict.edu 2015 International Conference on Computational Science and Computational Intelligence 978-1-4673-9795-7/15 $31.00 © 2015 IEEE DOI 10.1109/CSCI.2015.56 527 2015 International Conference on Computational Science and Computational Intelligence 978-1-4673-9795-7/15 $31.00 © 2015 IEEE DOI 10.1109/CSCI.2015.56 526