Advances in Visual Data Compression and Communication: by Feng Wu

By Feng Wu

Visible info is without doubt one of the richest and so much bandwidth-consuming modes of conversation. to fulfill the necessities of rising purposes, strong facts compression and transmission innovations are required to accomplish hugely effective verbal exchange, even within the presence of growing to be communique channels that supply elevated bandwidth.

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

The ebook reviews the drifting challenge in scalable video coding, analyzes the explanations inflicting the matter, and proposes a variety of recommendations to the matter. It explores the author’s Barbell-based lifting coding scheme that has been followed as universal software program by means 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 allows next entropy coding.

Exploring the visible correlation that exists in media, the textual content extends the present coding framework from varied facets, together with complicated photo synthesis—from description and reconstruction to organizing correlated pictures as a pseudo series. It explains the way to observe compressive sensing to unravel the knowledge compression challenge in the course of transmission and covers novel learn on compressive sensor information accumulating, random projection codes, and compressive modulation.

For analog and electronic transmission applied sciences, the publication develops the pseudo-analog transmission for media and explores state of the art examine on allotted pseudo-analog transmission, denoising in pseudo-analog transmission, and aiding MIMO. It concludes by way of contemplating rising advancements of data concept for destiny purposes.

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It is known as Shannon’s separate source channel. This separate result has some important practical implications. It implies that we can consider the design of a communications system as a combination of two parts, source coding and channel coding. We can design source codes for the most efficient representation of data. We separately and independently design channel codes appropriate for the channel. The combination will be as efficient as anything we could design by considering both problems together.

The set where f (s) > 0 is called the support set of S. 13) Ω where Ω is the support set of the random variable. √ 2 2 If S is a zero-mean Gaussian variable, that is, S ∼ f (s) = (1/ 2πσ 2 ) × e−s /2σ , h(S) = − =− f (s) ln f (s)ds, f (s) − √ s2 − ln 2πσ 2 ds, 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.

Clearly, we want to divide the real line into four regions and use a point within each region to represent the sample. But it is no longer immediately obvious what the representation regions and the reconstruction points should be. d. random variables drawn according to a given distribution. These random variables are to be represented using nR bits. , the samples are independent. It may appear that the representation of each sample is an independent problem to be treated separately. But this is not true as we will show at the end of this section.

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