Advances in Visual Data Compression and Communication: by Feng Wu

By Feng Wu

Visible info is among the richest and so much bandwidth-consuming modes of conversation. to satisfy the necessities of rising purposes, robust facts compression and transmission suggestions are required to accomplish hugely effective conversation, even within the presence of turning out to be conversation channels that supply elevated bandwidth.

Presenting the result of the author’s years of analysis on visible information compression and transmission, Advances in visible information Compression and conversation: assembly the necessities of recent functions presents a theoretical and technical foundation for complex examine on visible info compression and communication.

The ebook reports the drifting challenge in scalable video coding, analyzes the explanations inflicting the matter, and proposes a number of recommendations 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 diversified features, together with complicated photograph synthesis—from description and reconstruction to organizing correlated photos as a pseudo series. It explains the best way to practice compressive sensing to unravel the knowledge compression challenge in the course of transmission and covers novel learn on compressive sensor facts 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 learn on dispensed pseudo-analog transmission, denoising in pseudo-analog transmission, and helping MIMO. It concludes by means of contemplating rising advancements of knowledge conception for destiny purposes.

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In other words, the energy of every block becomes more compact after transform. This is convenient for subsequent quantization and entropy coding. Quantization in the hybrid video coding exploits visual redundancy. After DCT transform, each coefficient needs more than 10 bits to represent. The quantization objective is to map DCT coefficients from a large alphabet to a small alphabet. 4. 4 A scalar quantization. 5 The ZigZag scan. the minimum unit is quantized to ∆ . The parameter ∆ is selected according to the compression rate.

So it is called hybrid video coding. The temporal redundancies among video frames are exploited by motion estimation (ME) and motion compensation (MC). In the hybrid coding framework, every coded picture has to be reconstructed at both the encoder and the decoder. Furthermore, the reconstructed picture is required to be exactly the same at both the encoder and the decoder. It is an important assumption in hybrid video coding. 1 will find the most matched block in the reconstructed picture and record the motion vector (MV).

Second, we introduce the concept of channel capacity, which is the maximum rate that a channel can be used with negligible transmission errors, according to Shannon’s channel coding theorem. Hamming code is taken as an example to explain how channel coding works. Finally, the joint source and channel coding is introduced. The conditions for source channel separation are discussed. It is the theoretical justification why data compression and transmission can be studied independently. As a concrete application of source coding, Chapter 2 discusses the basic framework of video compression and the major developments in recent decades.

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