By Feng Wu
Visible details is among the richest and so much bandwidth-consuming modes of conversation. to fulfill the necessities of rising functions, robust info compression and transmission innovations are required to accomplish hugely effective conversation, even within the presence of transforming into conversation channels that provide elevated bandwidth.Presenting the result of the author's years of analysis on visible data compression and transmission, Advances in visible information Compression and conversation: assembly the necessities of latest purposes presents a theoretical. Read more...
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Extra info for Advances in Visual Data Compression and Communication: Meeting the Requirements of New Applications
Arithmetic coding achieves this goal. Assume sn = s1 s2 · · · sn is a source sequence. The essential idea of arithmetic coding is to efficiently calculate the PMF p(sn ) and the cumulative distribution function F(sn ) = ∑un ≤sn p(un ) for the source sequence sn . We can use a number in the interval [(F(sn ) − p(sn )], [F(sn )] as the codeword for sn . The codewords for various sequences are different because they are within different interval [(F(sn ) − p(sn )], [F(sn )]. A simplified version of the arithmetic coding is described here to illustrate the primary.
5% of all mobile traffic in 2016. Therefore, no matter how fast wired and wireless network bandwidth is being increased, highly efficient compression of visual data and its highly efficient transmission will need to continuously improve in quality. The theoretical basis of compression and transmission of visual data is Shannon’s information theory . 1. A transmitter sends a source S over a noisy channel to a receiver. The channel adds a certain noise to the transmitted source. 1 The basic framework for source and channel coding.
1 Introduction 5 The definition of entropy can be extended to a pair of random variables Si and S j . In information theory, the relationship between two random variables is described by joint entropy and conditional entropy. Definition 2 The joint entropy H(Si , S j ) of a pair of discrete random variables (Si , S j ) with a joint distribution p(si , s j ) is defined as H(Si , S j ) = − ∑ ∑ p(si , s j ) log p(si , s j ) = −E log p(Si , S j ). 2) si ∈A s j ∈A The conditional entropy of Si given S j is defined as the expected value of the entropies of the conditional distributions.