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By Wu F.
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Extra resources for Advances in visual data compression and communication
The above procedure depends on a model for which we can easily compute p(sn ). d. 24) i=1 and the Markov source, where n p(sn ) = p(s1 ) ∏ p(si j si 1 ). 25) i=2 In both cases, we can easily calculate p(sn sn+1 ) from p(sn ). Note that it is not essential that the probabilities used in the encoding be equal to the true distribution of the source. In some cases, such as in image and video compression, it is difficult to describe a true distribution for the source. Even then, it is possible to apply the above arithmetic coding procedure.
As a concrete application of channel coding, Chapter 3 discusses analog and digital transmission technologies. It is the technical basis for our research presented in Parts V and VI of this book. The analog transmission is introduced, although it is almost thrown away by academics and industry experts because of the dominance of digital systems. But some recent research shows that using the idea of analogy methods applied to modern digital communication have some unique advantages, especially for visual data communication.
Second, MPEG-4 introduces global motion compensation, which allows to express the effect of camera motion by using only a small number of parameters. Third, different VLC tables can be selected where the codes are designed for more efficiently encoding at ranges of lower or higher rates. The selection is controlled by the encoder and depends on the target rate. Finally, the direct mode can determine the motion vectors within B pictures by inferencing from the co-located motion vectors in P picture without rate overhead.