New PDF release: Advances in Visual Data Compression and Communication:

By Feng Wu

ISBN-10: 1482234157

ISBN-13: 9781482234152

Visible info is likely one of the richest and so much bandwidth-consuming modes of verbal exchange. to satisfy the necessities of rising purposes, robust info compression and transmission strategies are required to accomplish hugely effective communique, even within the presence of becoming communique channels that supply elevated bandwidth.

Presenting the result of the author’s years of analysis on visible facts compression and transmission, Advances in visible info Compression and communique: assembly the necessities of recent functions offers a theoretical and technical foundation for complicated learn on visible information compression and communication.

The publication stories the drifting challenge in scalable video coding, analyzes the explanations inflicting the matter, and proposes numerous ideas to the matter. It explores the author’s Barbell-based lifting coding scheme that has been followed as universal software program by way of MPEG. It additionally proposes a unified framework for deriving a directional remodel from the nondirectional counterpart. The constitution of the framework and the statistic distribution of coefficients are just like these of the nondirectional transforms, which enables next entropy coding.

Exploring the visible correlation that exists in media, the textual content extends the present coding framework from various features, together with complex snapshot synthesis—from description and reconstruction to organizing correlated photographs as a pseudo series. It explains find out how to follow compressive sensing to unravel the information compression challenge in the course of transmission and covers novel examine on compressive sensor info accumulating, random projection codes, and compressive modulation.

For analog and electronic transmission applied sciences, the ebook develops the pseudo-analog transmission for media and explores state-of-the-art study on disbursed pseudo-analog transmission, denoising in pseudo-analog transmission, and helping MIMO. It concludes through contemplating rising advancements of knowledge concept for destiny purposes.

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

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Instead, we try to highlight the core ideas behind the theory and make them more intuitively understood for further practical research on visual data compression and communication. The readers, who are interested in the entire information theory, can find information on this subject by Shannon [4] and Cover and Thomas [5]. Let us assume a finite symbol alphabet A = {a0 , a1 , · · · , aq−1 }, whose probabilities of occurrence are p = {p0 , p1 , · · · , pq−1 }, satisfying ∑i pi = 1. These probabilities are known.

The optimal expected length L∗ is impossibly less than the entropy H(S). In other words, the lossless compression of a random variable cannot be less than its entropy. Note that there is an overhead which is at most 1 bit because of the fact that log 1/pi is not always an integer. Let us take a simple example with four symbols A = {a0 , a1 , a2 , a3 }. 125}. According to Eq. 75 bits. 2 2 4 4 8 8 The most straightforward way to conduct source coding is to assign 2 different bits to every symbol.

Furthermore, for any i = j, Si is independent of S j . ) source. In information theory, the analog of the law of large numbers is the asymptotic equipartition property (AEP). d. 12) in probability. It is a direct consequence of the weak law of large numbers. 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 .

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

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