Data Compression
Networks and the Internet have generally been used to transmit data, which is relatively easy to transmit as it is in a digital format, and, in most cases, does not have a large requirement in the amount of data transmitted from each uses. Sometime application programs need to send more data in a timely fashion than the bandwidth: of a network supports. For example, a video application might have a 10 Mbps vide stream that it wants to transmit, but it has only a 1 Mbps network available to it. A anyone who has used the Internet knows, it is rare that you can move data between two points in the Internet at anything close to 1 Mbps. Furthermore, the resource allocation model of the Internet at the time of writing depends heavily on the fact that individual applications do not use much more than their "fair share" of the bandwidth on a congested link. For all these reasons, it is often important to first compress the data at the sender, then transmit it over the network, and finally to decompress it
the receiver.
Data compression falls into two broad categories. The first, called lossless compression, ensures that the data recovered from the compression /decompression process is exactly the same as the orginal data. In other words, no data is lost and the
decompressed data are identical to the orginal uncompressed data. In contrast, loss compression does not promise that the data received is exactly the same as the dal sent. For example, for image or video compression, the criterion may be that the decompressed image is indistinguishable from the original to the human eye.
It might seem that compressing your data before sending it would always be good idea, since the network would be able to deliver the compressed data in less tin than the uncompressed data. However, this is not necessarily the case, compression: decompression algorithms often involve time-consuming computations. The question you have to ask is whether the time it takes to compress/decompress the data worthwhile or not given such factors as the host's processor speed and the network bandwidth. Specifically, if Bc is the average bandwidth at which data can be push( through the compressor or decompressor (in series), Bn is the network bandwidth (including network processing costs) for uncompressed data and r is the average compression ratio, and if we assume that all the data are compressed before any of is transmitted, then the time taken to send x bytes of uncompressed data is
xlBn whereas the time to compress it and send the compressed data is 64

For example, for a compression ratio of 2, Bc would have to be greater than 2 x Bn for compression to make sense.
For many compression algorithms, we may not need to compress the whole data set before beginning transmission (videoconferencing would be impossible if we did), but rather we need to collect some amount of data (perhaps a few frames of video) first. The amount of data needed to "fill the pipe" in this case would be used as the value of x in the above equation.
Of course, when talking about lossy compression algorithms, processing resources are not the only factor. Depending on the exact application users are willing to make very different trade-off between bandwidth (or delay) and the extent of information loss due to compression. For example, a radiologist reading a mammogram is unlikely to tolerate any significant loss of image quality but might well tolerate a delay of several hours in retrieving an image over a network. By contrast, it has become quite clear that many people will tolerate questionable audio quality in exchange for free global telephone calls (not to mention the ability to talk on the phone while driving).
LOSSLESS COMPRESSION ALGORITHMS
We begin by introducing three lossless compression algorithms. We do not describe these algorithms in much detail-we just give the essential idea-since it is the lossy algorithms used to compress the image and video data that are of the greatest utility in today's network environment. We do comment, though, on how well these lossless algorithms work on digital imagery. Some of the ideas exploited by these lossless techniques show up again in later sections when we consider the lossy algorithms that are used to compress images.

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