compression is useful for images where pixel-to-pixel variation is small or zero, such as text or graphic images, for example for computer screen dumps as shown in Figure 5.19. Figure 5.19 A screen dump of the author's screen as text is being typed From Analogue to Digital Lossy Image Coding Images such as Figure 5.19 can be compressed with a large degree of efficiency. However, many images such as photographs of nature usually have large pixel transitions and therefore lossless differential coding does not yield significant compression. Further compression is achieved if some loss can be tolerated. Similar to audio and voice, lossy compression techniques take human perceptual ability into account. Our eyes can distinguish small colour transmissions across pixels but high fre- quency transitions are not well perceived. Image compression is principally carried out by removing these perceptually insignificant data. The process of identifying and removing high frequency components of an image is carried out in the frequency domain. First an image is transformed from the spatial domain into the frequency domain, then higher frequency transitions are identified and removed according to a desired compression level. Frequency domain representation of a spatial image is done through the discrete cosine transformation (DCT) technique. DCT in effect translates an image into a sum of many pos- sible pixel transitions. Figure 5.20 shows an 8x8 pixel grey-scale image. This image may be represented as sum of possible 8 pixel grey-scale transitions as shown in Figure 5.21. The DCT technique converts the spatial domain image of Figure 5.20 into a weighted value of all frequency components as shown in Figure 5.21. The image file can be represented in two ways. One is in the spatial domain where the grey-scale of each pixel is found. The other way is in the frequency domain where the weight of each frequency transition is found. Both equally represent the same image [24]. Figure 5.22 shows two images. Figure 5.22(a) has few black 