Data Compression Techniques
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 seminar paper Active In SP Posts: 6,455 Joined: Feb 2012 01-03-2012, 11:41 AM to get information about the topic data compression techniques full report ppt and related topic refer the link bellow topicideashow-to-data-compression-techniques--5444 topicideashow-to-data-compression-full-download-seminar and presentation-report-and-paper-presentation topicideashow-to-data-compression-and-huffman-algorithm topicideashow-to-a-review-of-data-compression-techniques topicideashow-to-lossless-data-compression-algorithms topicideashow-to-lossless-data-compression-algorithms
 seminar paper Active In SP Posts: 6,455 Joined: Feb 2012 16-03-2012, 02:32 PM Introduction to Data Compression   compression.pdf (Size: 376.64 KB / Downloads: 31) Introduction Compression is used just about everywhere. All the images you get on the web are compressed, typically in the JPEG or GIF formats, most modems use compression, HDTV will be compressed using MPEG-2, and several file systems automatically compress files when stored, and the rest of us do it by hand. The neat thing about compression, as with the other topics we will cover in this course, is that the algorithms used in the real world make heavy use of a wide set of algorithmic tools, including sorting, hash tables, tries, and FFTs. Furthermore, algorithms with strong theoretical foundations play a critical role in real-world applications. Information Theory 2.1 Entropy Shannon borrowed the definition of entropy from statistical physics to capture the notion of how much information is contained in a and their probabilities. For a set of possible messages S, Shannon defined entropy1 as, The Entropy of the English Language We might be interested in how much information the English Language contains. This could be used as a bound on how much we can compress English, and could also allow us to compare the density (information content) of different languages. Conditional Entropy and Markov Chains Often probabilities of events (messages) are dependent on the context in which they occur, and by using the context it is often possible to improve our probabilities, and as we will see, reduce the entropy. The context might be the previous characters in text (see PPM in Section 4.5), or the neighboring pixels in an image (see JBIG in Section 4.3).