By K.N. Ngan, T. Meier, D. Chai
In recent times, the paradigm of video coding has shifted from that of a frame-based method of a content-based technique, rather with the finalization of the ISO multimedia coding commonplace, MPEG-4. MPEG-4 is the rising ordinary for the coding of multimedia content material. It defines a syntax for a collection of content-based functionalities, particularly, content-based interactivity, compression and common entry. in spite of the fact that, it doesn't specify how the video content material is to be generated. To generate the video content material, video needs to be segmented into video items and tracked as they transverse around the video frames. This publication addresses the tough challenge of video segmentation, and the extraction and monitoring of video item planes as outlined in MPEG-4. It then makes a speciality of the categorical factor of face segmentation and coding as utilized to videoconferencing so that it will enhance the standard of videoconferencing photos specifically within the facial zone. Modal-based coding is a content-based coding process used to code artificial gadgets that experience turn into an enormous a part of video content material. It leads to super low bit premiums simply because merely the parameters had to signify the modal are transmitted. Model-based coding is incorporated to supply heritage info for the artificial item coding in MPEG-4. finally, MPEG-4, the 1st coding normal for multimedia content material is defined intimately. the themes coated contain the coding of audio gadgets, the coding of common and artificial video items, and blunder resilience. complex Video Coding is without doubt one of the first books on content-based coding and MPEG-4 coding commonplace. It serves as an exceptional info resource and reference for either researchers and practising engineers.
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Extra info for Advanced Video Coding: Principles and Techniques: The Content-based Approach (Advances in Image Communication, Volume 7)
This makes ICM converge significantly faster, but at the cost of settling in a local minimum of the cost function. Consider an image restoration problem where O denotes the degraded image and X the unknown original image to be estimated. Typically, X is assumed to be a sample of an MRF and therefore P(X) is a Gibbs distribution. 12) all (i, j) with 1 ((O(i,j)-X(i,j)) 2a 2 f(O(i,j)lX(i,j)) - x/27ra2 exp - 2) . 13) Similarly to the Gibbs sampler, the update of pixel (i, j) is based on the local conditional probability P(X(i, j) I O, X (k, 1), all (k, l) ~ (i, j)).
3 Marker Extraction After simplifying the image, the marker extraction step detects the presence of uniform areas. Each of these markers forms an initial seed for a region in the final segmentation. This step also decides implicitly how many regions there will be in the final partition. Notice that marker extraction is not concerned with the location of region boundaries. This will be accomplished by the watershed algorithm in the next step. Consequently, markers typically consist only of the interior of regions.
28 CHAPTER 1. IMAGE AND VIDEO SEGMENTATION Each marker obtained by the previous marker extraction step results in one region or basin. Because normally large flat zones are selected as markers, the morphological gradient in their interior will be zero. Consequently, these markers correspond to minima in the relief (see Fig. 5). The watershed algorithm can now be viewed as a flooding procedure. Starting from the lowest altitude, the water gradually fills up the first catchment basin. When the water level of this basin reaches the altitude of another minimum, water also starts filling up that basin.