TABLE OF CONTENTS Virtually all compression systems rely on some combination of the basic processes outlined in this chapter. In order to understand compression a good grasp of filtering and transforms is essential along with motion estimation for video applications. These processes only express the information in the best way for the actual compression stage, which almost exclusively begins by using requantizing to shorten the wordlength. In this chapter the principles of filters and transforms will be explored, along with motion estimation and requantizing. These principles will be useful background for the subsequent chapters.
In compression systems it is very important to remember that the valuable commodity is information. For practical reasons the way information is represented may have to be changed a number of times during its journey. If accuracy or realism is a goal these changes must be well engineered. In the course of this book we shall explore changes between the continuous domain of real sounds and images, the discrete domain of sampled data and the discrete spatial and temporal frequency domains. A proper understanding of these processes requires familiarity with the principles of filters and transforms.
Figure 3.1 shows an optical system of finite resolution. If an object containing an infinitely sharp line is presented to this system, the image will be an intensity function known in optics as a point spread function. Such functions are almost invariably symmetrical in optics. There is no movement or change here, the phenomenon is purely spatial. A point spread...
