Basic Techniques.- Statistical Methods.- Dictionary Methods.- Image Compression.- Wavelet Methods.- Video Compression.- Audio Compression.- Other Methods.
4 Image Compression (p. 251- 252)
The first part of this chapter discusses the basic features and types of digital images and the main approaches to image compression. This is followed by a description of about 30 different compression methods. The author would like to start with the following observations:
1. Why were these particular methods included in the book, while others were ignored? The simple answer is: Because of the documentation available to the author. Image compression methods that are well documented were included. Methods that are kept secret, or whose documentation was not clear to the author, were left out.
2. The treatment of the various methods is uneven. This, again, reflects the documentation available to the author. Some methods have been documented by their developers in great detail, and this is reflected in this chapter. Where no detailed documentation was available for a compression algorithm, only its basic principles are outlined here.
3. There is no attempt to compare the various methods described here. This is because most image compression methods have been designed for a particular type of image, and also because of the practical difficulties of getting all the software and adapting it to run on the same platform.
4. The compression methods described in this chapter are not arranged in any particular order. After much thought and many trials, the author gave up any hope of sorting the compression methods in any reasonable way. Readers looking for any particular method can use the table of contents and the detailed index to easily locate it.
A digital image is a rectangular array of dots, or picture elements, arranged in m rows and n columns. The expression m×n is called the resolution of the image, and the dots are called pixels (except in the cases of fax images and video compression, where they are referred to as pels). The term "resolution" is sometimes also used to indicate the number of pixels per unit length of the image. Thus, dpi stands for dots per inch. For the purpose of image compression it is useful to distinguish the following types of images:
1. A bi-level (or monochromatic) image. This is an image where the pixels can have one of two values, normally referred to as black and white. Each pixel in such an image is represented by one bit, so this is the simplest type of image.
2. A grayscale image. A pixel in such an image can have one of the n values 0 through n . 1, indicating one of 2n shades of gray (or shades of some other color). The value of n is normally compatible with a byte size, i.e., it is 4, 8, 12, 16, 24, or some other convenient multiple of 4 or of 8. The set of the most-significant bits of all the pixels is the most-significant bitplane. Thus, a grayscale image has n bitplanes.
3. A continuous-tone image. This type of image can have many similar colors (or grayscales). When adjacent pixels differ by just one unit, it is hard or even impossible for the eye to distinguish their colors. As a result, such an image may contain areas with colors that seem to vary continuously as the eye moves along the area. A pixel in such an image is represented by either a single large number (in the case of many grayscales) or by three components (in the case of a color image). A continuous-tone image is normally a natural image (natural as opposed to artificial), and is obtained by taking a photograph with a digital camera, or by scanning a photograph or a painting. Figures 4.57 through 4.60 are typical examples of continuous-tone images.
