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Why is data compression so important?

Television quality video requires approximately 100 G Bytes for each hour or about 7 M bytes for each second. Such data and rates severely stress storage systems and networks that make even the most even the most trivial real-time processing impossible without special purpose hardware. Data compression requires the identification of the source of redundancy. In other words data compression seeks to reduce the number of bits used to store or transmit information.

There are two main type of data compression lossless and lossy compression. Lossless compression can recover the exact original data after decompression. It is used mainly for compressing databases records, spreadsheets or word processing files, where an exact replica of the original is essential. On the other hand lossy compression will result in a certain loss of accuracy in exchange or a substantial increase in compression. Lossy compression is more effective when used to compress graphic images and digitised voice, where losses outside visual and aural perception can be tolerated. Most levels of lossy compression techniques can be adjusted to achieve different quality levels, gaining high accuracy in exchange for less effective compression.

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Lossless data compression is used in software compression tools such as the highly popular Zip format, used by PKZIP and WinZip, and the Unix programs bzip, gzip and compress. Lossless compression is used when it is important that the original and the decompressed data are exactly identical, or when no assumption can be made on whether certain deviation is uncritical. Typical examples are executable programs and source code. Some image file formats, notably PNG, use only lossless compression, while others like TIFF and MNG may use either lossless or lossy methods. GIF uses a technically lossless compression method, but most GIF implementations are incapable of representing full colour, so they quantize the image (often with dithering) to 55 or fewer colors before encoding as GIF. Colour quantization is a lossy process, but reconstructing the colour image and then re-quantizing it produces no additional loss. (Some rare GIF implementations make multiple passes over an image, adding 55 new colors on each pass.)

Lossless data compression algorithms cannot guarantee to compress (that is make smaller) all input data sets. In other words for any (lossless) data compression algorithm there will be an input data set that does not get smaller when processed by the algorithm. This is easily proven with elementary mathematics using a counting argument

Lossless compression methods may be categorized according to the type of data they are designed to compress. The three main types of targets for compression, algorithms are text, images, and sound. Whilst, in principle, any general-purpose lossless compression algorithm (general-purpose means that they can handle all binary input) can be used on any type of data, many are unable to achieve significant compression on data that is not of the form that they are designed to deal with. Sound data, for instance, cannot be compressed well with conventional text compression algorithms.

Most lossless compression programs use two different kinds of algorithm One which generates a statistical model for the input data, and another which maps the input data to bit strings using this model in such a way that probable (e.g. frequently encountered) data will produce shorter output than improbable data. Often, only the former algorithm is named, while the second is implied or unspecified.

MPEG is a compression standard for digital video sequences, such as used in computer video and digital television networks. In addition, MPEG also provides for the compression of the sound track associated with the video. The name comes from its originating organization, the Moving Pictures Experts Group. If you think JPEG is complicated, MPEG is a nightmare! MPEG is something you buy, not try to write yourself. The future of this technology is to encode the compression and de-compression algorithms directly into integrated circuits. The potential of MPEG is vast. Think of thousands of video channels being carried on a single optical fibre running into your home. This is a key technology of the 1st century.

In addition to reducing the data rate, MPEG has several important features. The movie can be played forward or in reverse, and at either normal or fast speed. The encoded information is random access, that is, any individual frame in the sequence can be easily displayed as a still picture. This goes along with making the movie editable, meaning that short segments from the movie can be encoded only with reference to themselves, not the entire sequence. MPEG is designed to be robust to errors. The last thing you want is for a single bit error to cause a disruption of the movie.

The approach used by MPEG can be divided into two types of compression within-the-frame and between-frame. Within-the-frame compression means that individual frames making up the video sequence are encoded as if they were ordinary still images. This compression is preformed using the JPEG standard, with just a few variations. In MPEG terminology, a frame that has been encoded in this way is called an intra-coded or I-picture.

Most of the pixels in a video sequence change very little from one frame to the next. Unless the camera is moving, most of the image is composed of a background that remains constant over dozens of frames. MPEG takes advantage of this with a sophisticated form of delta encoding to compress the redundant information between frames. After compressing one of the frames as an I-picture, MPEG encodes successive frames as predictive-coded or P-pictures. That is, only the pixels that have changed since the I- picture are included in the P-picture.

While these two compression schemes form the backbone of MPEG, the actual implementation is immensely more sophisticated than described here. For example, a P-picture can be referenced to an I-picture that has been shifted, accounting for motion of objects in the image sequence. There are also bidirectional predictive-coded or B-pictures. These are referenced to both a previous and a future I-picture. This handles regions in the image that gradually change over many of frames. The individual frames can also be stored out-of-order in the compressed data to facilitate the proper sequencing of the , P, and B-pictures. The addition of colour and sound makes this all the more complicated.

The main distortion associated with MPEG occurs when large sections of the image change quickly. In effect, a burst of information is needed to keep up with the rapidly changing scenes. If the data rate is fixed, the viewer notices blocky patterns when changing from one scene to the next. This can be minimized in networks that transmit multiple video channels simultaneously, such as cable television. The sudden burst of information needed to support a rapidly changing scene in one video channel, is averaged with the modest requirements of the relatively static scenes in the other channels.

A signal is any change in a measurable condition that is used to represent information. There is an almost endless number of ways we can send signals; however, they all require that we use some sort of energy to make a change that can be detected by someone or something. We can make sounds, wave flags, post signs, or use hand gestures. All of these can convey information, as long as both the sender and recipient agree on the meaning of the signal.

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