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Compression algorithms reduce the number of bytes required to represent data and the amount of memory required to store images. Compression allows a larger number of images to be stored on a given medium and increases the amount of data that can be sent over the internet.
In the mid-1980s, following work by Terry Welch, the LempelZivWelch (LZW) algorithm rapidly became the method of choice for most general-purpose compression systems. LZW is used in GIF images, programs such as PKZIP, and hardware devices such as modems.
ZIP is a simple matching algorithm using two sliding windows, called the base window and the look-ahead window. These two windows are placed side-by-side on the data file, where the look-ahead window goes ahead of the base window. ZIP scans the entire file by sliding these two windows and encoding data on the fly.
In the coding step we use shorter code words to represent letters that occur more frequently, thus lowering the average number of bits required to represent each letter. Compression in all its forms exploits structure, or redundancy, in the data to achieve a compact representation.
Data compression algorithms reduce the size of the bit strings in a data stream that is far smaller in scope and generally remembers no more than the last megabyte or less of data. File-level deduplication eliminates redundant files and replaces them with stubs pointing to the original file.
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Some of the most common and popular algorithms are ZIP, JPEG, MP3, GZIP, and LZ4. ZIP is a lossless, dictionary-based compression method that is widely used for compressing and archiving files and folders and supports various compression levels and encryption options.
Non-repetitive parts of the DNA are generally compressed using dynamic programming, by dividing the sequences in square matrices which contain common repeat of a single base and then substituting the matrix with the base and putting the order of the matrix in a string.
There are two types of compression: lossless and lossy.

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