Complexity analysis and accuracy of image recovery based on signal transformation algorithms

Abstract

In this paper we compare and analyze the complexity of three functions: Fast Fourier transform (FFT), Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT), used in image transformation. The purpose of all the algorithms is to shift the signal from space or time domain to frequency domain for de-noising or compression. We compare the simulated process time of both one and two dimensional FFT, DCT and DWT (Symlet and Debauches 1) using image and speech signal. The process time is found lowest for FFT and highest for DWT, provided its basis function governs the process time and DCT provide the moderate result. Finally the quality of compressed image under the three mathematical functions are compared, where DWT is found as the best and FFT yields worst result.