The Essential Guide To Video Processing by Alan C. Bovik

By Alan C. Bovik

This finished and state-of-the paintings method of video processing supplies engineers and scholars a accomplished creation and comprises complete insurance of key purposes: instant video, video networks, video indexing and retrieval and use of video in speech processing. Containing all of the crucial equipment in video processing along the most recent criteria, it's a whole source for the pro engineer, researcher and graduate student.

  • Numerous conceptual and numerical examples
  • All the newest criteria are completely lined: MPEG-1, MPEG-2, MPEG-4, H.264 and AVC
  • Coverage of the newest thoughts in video security

"Like its sister quantity "The crucial advisor to photograph Processing," Professor Bovik’s crucial consultant to Video Processing offers a well timed and entire survey, with contributions from prime researchers within the quarter. hugely suggested for everybody with an curiosity during this attention-grabbing and fast-moving field." ―Prof. Bernd Girod, Stanford collage, USA

* Edited through a number one individual within the box who created the IEEE foreign convention on snapshot Processing, with contributions from specialists of their fields.
* a number of conceptual and numerical examples
*All the newest criteria are completely coated: MPEG-1, MPEG-2, MPEG-4, H.264 and AVC.
* assurance of the newest concepts in video protection

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2) where the constants Z and T are called the partition function and temperature, respectively, and the energy function U is of the form U (␷) ϭ V (␷, c). c∈C where V (␷, c) is called a potential function, and depends only on the value of ␷ at sites that belong to the clique c. For two-element cliques {n, l} considered here, we will explicitly write V (␷[n], ␷[l]). The equivalence between Markov random fields and Gibbs distributions is provided through the important Hammersley-Clifford theorem, which states that ⌼ is a MRF on ⌳ with respect to N if and only if its probability distribution is a Gibbs distribution with respect to ⌳ and N .

The motivation is that in stationary areas only camera noise will distinguish same-position pixels at tkϪ1 and tk , whereas in moving areas this difference is attributed to motion and therefore unpredictable. Let ␳k [n] ϭ Ik [n] Ϫ IkϪ1 [n] be an observation, upon which we intend to select one of the two hypotheses. 7) S √ where ␪ ϭ 2␴ 2 ln(␽ · 2L · ␲S /( 2␲␴ 2 · ␲M ). 7) ␳k2 [n] is replaced by |␳k [n]| and ␪ is computed accordingly. Such a test was used in some early motion detection algorithms.

1 In the sequel, the dependence of d on t and ␶ will be dropped whenever it is clear between what time instants d applies. 3 Region of Support The set of points x to which spatial and temporal motion models apply is called the region of support, denoted by R. The selection of a motion model and region of support is one of the major factors determining the precision of the resulting motion parameter estimates. Usually, for a given motion model, the smaller the region of support R, the better the approximation of motion.

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