![]() ![]() And, lossy algorithms typically achieve much better compression ratios than do their lossless counterparts they can be an order of magnitude better or more. They are also very good at filling in missing pieces and even correcting some errors in what we see or hear. Our senses and brains can only perceive so much detail. As noted above, this is because multimedia data often contains information that is of little utility to the human who receives it. In contrast, lossy compression, commonly used for multimedia data, does not promise that the data received is exactly the same as the data sent. It turns out that the techniques used for compressing data-which are typically lossless, because most people don't like to lose data from a file-also show up as part of the solution for multimedia compression. The uses of compression are not limited to multimedia data of course-for example, you may well have used a utility like zip or compress to compress files before sending them over a network, or to uncompress a data file after downloading. ![]() In this section, we'll look at some of the major efforts in representing and compressing multimedia data. Hence, both computer science and the study of human perception come into play. ![]() You want to try to keep the information that is most important to a human, while getting rid of anything that doesn't improve the human's perception of the visual or auditory experience. Because multimedia data is consumed mostly by humans using their senses-vision and hearing-and processed by the human brain, there are unique challenges to compressing it. ![]() Part of what has made the widespread transmission of multimedia across networks possible is advances in compression technology. Multimedia data, comprised of audio, video, and still images, now makes up the majority of traffic on the Internet. Image Representation and Compression (GIF, JPEG). ![]()
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