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Data Compression Techniques
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Data compression is the process of converting an input data stream or the source stream or the original raw data into another data stream that has a smaller size. data compression is popular because of two reasons

1) People like to accumulate data and hate to throw anything away. No matter however large a storage device may be, sooner or later it is going to overflow. Data compression seems useful because it delays this inevitability
2) People hate to wait a long time for data transfer. There are many known methods of data compression. They are based on the different ideas and are suitable for different types of data.

They produce different results, but they are all based on the same basic principle that they compress data by removing the redundancy from the original data in the source file. The idea of compression by reducing redundancy suggests the general law of data compression, which is to "assign short codes to common events and long codes to rare events". Data compression is done by changing its representation from inefficient to efficient form.

The main aim of the field of data compression is of course to develop methods for better and better compression. Experience shows that fine tuning an algorithm to squeeze out the last remaining bits of redundancy from the data gives diminishing returns. Data compression has become so important that some researches have proposed the "simplicity and power theory". Specifically it says, data compression may be interpreted as a process of removing unnecessary complexity in information and thus maximizing the simplicity while preserving as much as possible of its non redundant descriptive power.

Basic Types Of Data Compression

There are two basic types of data compression.
1. Lossy compression
2. Lossless compression

Lossy Compression
In lossy compression some information is lost during the processing, where the image data is stored into important and unimportant data. The system then discards the unimportant data
It provides much higher compression rates but there will be some loss of information compared to the original source file. The main advantage is that the loss cannot be visible to eye or it is visually lossless. Visually lossless compression is based on knowledge about colour images and human perception.

Lossless Compression
In this type of compression no information is lost during the compression and the decompression process. Here the reconstructed image is mathematically and visually identical to the original one. It achieves only about a 2:1 compression ratio. This type of compression technique looks for patterns in strings of bits and then expresses them more concisely.

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