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enlarged a small section of the image to illustrate how the JPEG compression algorithm actually works. Keep in mind that a digital image comprises a large number of individual pixels, each of which has individual color characteristics associated with it. In the original photograph, Steve s skin tones are relatively uniform across his face except for the areas that are in shadow, such as his eyes and parts of his chin. However, when we enlarge a small area of the image, the pixels vary widely in terms of their color components. This disparity is what JPEG exploits as a way of getting the job done. It knows that the human eye cannot see the difference between these pixels until they are dramatically enlarged, as I have done in Figure 2-21. What JPEG does is cluster the pixels in 16-by-16 pixel groups. It then mathematically removes every other pixel, resulting in eight-by-eightpixel arrays. It then performs a mathematical transformation of the 64pixel arrays called a discrete cosine transform that calculates an average
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Figure 2-20 My son, Steve
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Downloaded from Digital Engineering Library @ McGraw-Hill (www.digitalengineeringlibrary.com) Copyright 2004 The McGraw-Hill Companies. All rights reserved. Any use is subject to the Terms of Use as given at the website.
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Figure 2-21 Steve s chin, enlarged to show color ranges among pixels
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value for each group of pixels. Additional steps in the process further reduce the pixel count, resulting in a dramatic reduction in the total number of pixels required to represent the original image hence the term lossy. To restore the image, the reverse process is invoked. JPEG is a 24-bit color scheme that yields high degrees of color fidelity. Unfortunately, many users do not have access to 24-bit color monitors, which means that a JPEG-encoded image will not display the image correctly. To display a full-color image, the host computer must analyze the file, choose an appropriate set of representative colors from a color chart, and map the image into the new color domain. This process is known as color quantization. The speed and image quality of a JPEG viewer used on a lower quality machine is largely determined by its quantization algorithm. If the algorithm is complex and well designed, the user will see minimal impact on the image from the quantization process. If the encoder is inferior, however, the result will be equally inferior. Of course, there are ways around this. GIF image are already quantized to 256 or fewer colors (GIF doesn t allow more than 256 color palette entries.) GIF has the advantage that the creator of the image calculates the degree of color quantization so that viewers do not have to. For this reason, GIF viewers are typically faster than JPEG viewers. Of course, this also limits the user to whatever constraints the GIF viewer places on the image. If the creator of the image quantized the original image to a level that cannot be achieved by a receiving device, the image will have to be further quantized, resulting in poorer image quality.
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GIF clearly offers some advantages over JPEG, but in the long term JPEG offers better image quality than GIF. Of course, eight-bit displays are rapidly disappearing from the landscape in favor of higher resolution monitors. For them, GIF has already become an academic argument because JPEG is a far better solution for image display.
The Graphics Interchange Format (GIF)
GIF defines a protocol designed to transmit raster-based graphic data that is independent of the hardware used to create or display them. GIF is defined in terms of blocks and subblocks that contain relevant information used to reproduce a graphic. CompuServe released GIF as a free and open specification in 1987. It soon became a global standard and also played an important role within the Internet community as users started to share graphics files. It was well supported by CompuServe s Information Service but did not require a subscription to CompuServe. The format was relatively simple and well documented. Like most graphic management tools, GIF compresses images to reduce the file size, using a technique called Lempel-Ziv-Welch (LZW) compression. Unisys holds a patent on the LZW procedure, which soon became a popular technique for data compression. GIF is not the only tool that relies on LZW; TIFF, described later, also includes LZW compression among its favored compression techniques.
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