ABSTRACT
In this chapter, the authors focus on the utility of the wavelet transform for image data compression applications. They introduce wavelet transform theory by starting with the short-time Fourier transform (STFT) and presents discrete wavelet transform. They also discuss the basic concept of image wavelet transform coding with an emphasis on embedded image wavelet transform coding algorithms, which is the base of Joint Photographic Experts Group 2000. For image coding, the wavelet transform is used to decompose the image data into wavelets. Since the middle of the 1980s, a number of signal processing applications have emerged using wavelet theory. The STFT uses a sinusoidal wave as its basis function. These basis functions keep the same frequency over the entire time interval. For image data, the concept of time-frequency plane becomes spatial-frequency plane. The main contribution of wavelet transform theory is that it provides an elegant framework in which both statistical behaviors of image data can be analyzed with equal importance.
