Advances in visual data compression and communication by Wu F.

By Wu F.

Show description

Read or Download Advances in visual data compression and communication PDF

Similar imaging systems books

Lossless Compression Handbook (Communications, Networking and Multimedia)

The 21 chapters during this guide are written by way of the prime specialists on the earth at the idea, ideas, purposes, and criteria surrounding lossless compression. As with such a lot utilized applied sciences, the factors part is of specific significance to training layout engineers. in an effort to create units and communique platforms that may speak and be appropriate with different structures and units, criteria needs to be undefined.

Three-Dimensional Holographic Imaging

A complete survey of the state-of-the-art in three-D holographic imaging innovations and functions This booklet introduces the overall techniques of either real-time and non-real-time 3-D holographic imaging thoughts for clinical and engineering functions. It deals readers a primary knowing of the recommendations of 3D holographic imaging in addition to reasonable layout and implementation.

Still Image and Video Compression with MATLAB

This publication describes the foundations of snapshot and video compression suggestions and introduces present and renowned compression criteria, equivalent to the MPEG sequence. Derivations of correct compression algorithms are constructed in an easy-to-follow model. a variety of examples are supplied in every one bankruptcy to demonstrate the options.

Additional resources for Advances in visual data compression and communication

Example text

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 = fa0 , a1 , , aq 1 g, whose probabilities of occurrence are p = fp0 , p1 , , pq 1 g, satisfying ∑i pi = 1. These probabilities are known.

It is from a finite alphabet and satisfies the AEP. The sequence of symbols Sn = fS1 , S2 , , Sn g is sent over the channel so that the receiver can reconstruct the sequence. Assume the one stage coding is considered. We map the sequence onto a codeword Y n (Sn ) and send the codeword over the channel. The receiver looks at his received sequence Yˆ n and makes an estimation Sˆn of the sequence Sn that was sent. The receiver makes an error if (n) Sˆn 6= Sn . 66) yˆn sn where I is the indicator function and g(yˆn ) is the decoding function.

13) Ω where Ω is the support set of the random variable. If S is a zero-mean Gaussian variable, that is, S h(S) = = f (s) ln f (s)ds, f (s) s2 2σ 2 ES2 1 + ln 2πσ 2 , 2σ 2 2 1 1 = + ln 2πσ 2 , 2 2 1 1 = ln e + ln 2πσ 2 , 2 2 1 = ln 2πeσ 2 . 19) Changing the base of the logarithm, the differential entropy for a zero-mean Gaussian variable is h(S) = 1/2 log 2πeσ 2 . 2 Source Coding With the above basic information theory definitions, we are ready to discuss source coding. The objective of the so-called source coding is to find a source code C mapping all possible values of a random variable S in the alphabet A to a set of finite length of binary strings (also called codewords).

Download PDF sample

Rated 4.29 of 5 – based on 16 votes