The lossy compression techniques presented so far have tried to exploit the fundamental mathematical properties of information (lossless coding), to model and approximate the properties of the signal directly (differential coding), and to model the creation of the signal (source coding, such as in speech compression). We also presented simple perceptual methods, such as the µ-law encoder.

The methods presented in this chapter use transformations modeled after how human sensory perception works, using a much greater sophistication level. These perceptual coders are so effective that they are used in virtually every device today that handles images or sound, from photo cameras to mobile phones to DVD players to mobile digital music players.

Before we introduce them, we recapitulate two fundamental signal transformations that are an important prerequisite for all the algorithms presented in this chapter, as well as for many of the analysis algorithms presented later. When explaining perceptual compression, two transformations are very important: the Discrete Fourier Transform (DFT) and the Discrete Cosine Transform (DCT), which are described in the following sections. Other transforms, such as the Discrete Wavelet Transforms (DWT), which are a generalization on the transforms mentioned, are also used in multimedia signal processing, and the references cited at the end of this chapter are well worth looking up.