Speech and image compression using discrete wavelet transform

The fast development of multimedia computing has led to the demand of using digital speech and images. The manipulation, storage and transmission of speech and images in their raw form is very expensive, and significantly slows the transmission and make storage costly. Efficient speech and image compression solutions are becoming critical with the recent growth of data intensive, multimedia based applications. Many techniques are now available and much effort is being expended in determining the optimum compression technique. Recently compression techniques using Wavelet Transform (WT) have received great attention, because of their promising compression ratio, ability to analyze the temporal and spectral properties of speech and image signals and flexibility in representing non stationary signals like speech and images by taking into account human perception system. In this paper we describe the application of Discrete Wavelet Transform (DWT) for analysis, processing and compression of multimedia signals like speech and image. More specifically we explore the major issues concerning the wavelet based speech and image compression which include choosing optimal wavelet, decomposition levels and thresholding criteria. The simulation results prove the effectiveness of DWT based techniques in attaining an efficient compression ratio of 2.31 for speech and 2.67 for images, achieving higher signal to noise ratio (SNR), better peak signal to noise ratio (PSNR), while the retained signal energy is 99.9885% and the resulting signals are generally much smoother.