Download e-book for iPad: Advances in Visual Data Compression and Communication: by Feng Wu

By Feng Wu

ISBN-10: 1439850461

ISBN-13: 9781439850466

ISBN-10: 1439855609

ISBN-13: 9781439855607

ISBN-10: 1439875146

ISBN-13: 9781439875148

ISBN-10: 1439883246

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ISBN-10: 1482234130

ISBN-13: 9781482234138

ISBN-10: 1482234157

ISBN-13: 9781482234152

Visible info is without doubt one of the richest and so much bandwidth-consuming modes of communique. to satisfy the necessities of rising functions, robust information compression and transmission suggestions are required to accomplish hugely effective conversation, even within the presence of growing to be conversation channels that supply elevated bandwidth.Presenting the result of the author's years of analysis on visible data Read more...

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Extra resources for Advances in Visual Data Compression and Communication: Meeting the Requirements of New Applications

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D. variables, 1/n ∑i Si is close to its expected value E(S) for large n. The AEP states that −1/n log p(S1 , S2 , · · · , Sn ) is close to the entropy H(S), where p(S1 , S2 , · · · , Sn ) is the probability of observing the sequence S1 , S2 , · · · , Sn . Thus, the probability p(S1 , S2 , · · · , Sn ) will be close to 2−nH(S) . Finally, we discuss the continuous random variable. Let S be a random variable with a cumulative distribution F(s) = Pr(S ≤ s). If F(s) is continuous, the random variable is said to be continuous.

The distortion incurred by such information loss is often invisible to HVS. 1 The framework of hybrid video coding. 4. Statistic redundancy — The symbols that describe visual information are not random. They present a strong nonuniform distribution. 2, they can be efficiently represented by source coding. All image and video compression schemes are designed in an effort to fully exploit the four types of redundancies. The target is to minimize distortion for a given compression rate or minimize compression rate for a given distortion.

3 Rate Distortion Theory In Huffman coding and arithmetic coding, the coding process does not lose any source information and the source can be exactly reproduced from coded data, known as lossless coding. In many cases, such as with image and video compression, some source information has to be discarded during the coding process so that the coding rate is not larger than a given rate. The decoded source always contains a certain difference from the original source. Therefore, this coding is called lossy coding.

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Advances in Visual Data Compression and Communication: Meeting the Requirements of New Applications by Feng Wu

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