
By Feng Wu
ISBN-10: 1439850461
ISBN-13: 9781439850466
ISBN-10: 1439855609
ISBN-13: 9781439855607
ISBN-10: 1439875146
ISBN-13: 9781439875148
ISBN-10: 1439883246
ISBN-13: 9781439883242
ISBN-10: 1482234130
ISBN-13: 9781482234138
ISBN-10: 1482234157
ISBN-13: 9781482234152
Visible info is among the richest and such a lot bandwidth-consuming modes of communique. to fulfill the necessities of rising functions, strong information compression and transmission recommendations are required to accomplish hugely effective verbal exchange, even within the presence of starting to be communique channels that supply elevated bandwidth.Presenting the result of the author's years of study on visible dataRead more...
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Extra resources for Advances in Visual Data Compression and Communication: Meeting the Requirements of New Applications
Sample text
By the comparison, we can clearly see the trend of the developments on video compression. 261 is the first standard based on the hybrid video coding framework, ratified in November 1988. It was originally designed for transmission over Integrated Services Digital Network (ISDN) lines on which data rates are multiples of 64 kbit/s. The input video resolutions are common intermediate format (CIF) and QCIF using a 4:2:0 format. 1. 261, only one reconstructed picture is used as reference for motion compensation.
8) si ∈A s j ∈A = si ∈A s j ∈A =− ∑ ∑ p(si , s j ) log p(si ) + si ∈A s j ∈A =− ∑ p(si ) log p(si ) − − si ∈A = H(Si ) − H(Si | S j ). 11) ✐ ✐ ✐ ✐ ✐ ✐ ✐ ✐ 6 1 Information Theory Next, we consider a set of random variables Sn = {S1 , S2 , · · · , Sn }. All random variables have the same distribution p(s). 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.
6) As we have discussed, entropy is a measure of the uncertainty of a random variable. It is also a measure of the amount of information required on average to describe the random variable. However, mutual information is a measure of the amount of information that one random variable contains about another random variable. It is the reduction in the uncertainty of one random variable due to the knowledge of the other. Definition 4 Assume two random variables Si and S j with a joint probability mass function (PMF) p(si , s j ).