By K.N. Ngan, T. Meier, D. Chai
Lately, the paradigm of video coding has shifted from that of a frame-based method of a content-based process, rather with the finalization of the ISO multimedia coding common, MPEG-4. MPEG-4 is the rising typical for the coding of multimedia content material. It defines a syntax for a collection of content-based functionalities, specifically, content-based interactivity, compression and common entry. besides the fact that, it doesn't specify how the video content material is to be generated. To generate the video content material, video should 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 specializes in the explicit factor of face segmentation and coding as utilized to videoconferencing with a purpose to enhance the standard of videoconferencing photos particularly within the facial zone.
Modal-based coding is a content-based coding strategy used to code man made items that experience turn into an incredible a part of video content material. It ends up in tremendous low bit charges simply because merely the parameters had to characterize the modal are transmitted. Model-based coding is integrated to supply heritage info for the substitute item coding in MPEG-4. finally, MPEG-4, the 1st coding general for multimedia content material is defined intimately. the subjects lined comprise the coding of audio items, the coding of typical and artificial video gadgets, and blunder resilience.
Advanced Video Coding is among the first books on content-based coding and MPEG-4 coding ordinary. It serves as an outstanding details resource and reference for either researchers and practising engineers.
Read Online or Download Advanced Video Coding: Principles and Techniques, Volume 7: The Content-based Approach PDF
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Extra info for Advanced Video Coding: Principles and Techniques, Volume 7: The Content-based Approach
43) CCCi,j The parameter/3, which is needed to evaluate Vc(x), is expected as an input parameter to the segmentation algorithm. 43) consists of a spatial continuity term and a closeto-data term. The spatial continuity term, derived from the Gibbs distribution, encourages adjacent pixels to have the same segmentation label. In fact, a partition consisting of one region only would yield the minimum cost. On the other hand, such a segmentation would not describe the observation O very well. The close-to-data term prefers a segmentation where (i, j) is assigned to the region that is closest with respect to the gray-level o(i, j).
Since unreliable pixels should not affect reliable pixels, the potential Vc(X) is set to zero for all cliques C that contain one or more pixels that are still uncommitted. The resulting function Ui,j(X(i,j)) is referred to as the local energy at site (i, j). It is easy to see that a low local energy corresponds to a high likelihood of the value X(i,j) and vice versa. The confidence c(i,j) of a committed site (i, j) is now defined as the difference between the current local energy and the minimum local energy.
Efficient implementations of the watershed algorithm rely on clever scanning. 35), they make use of hierarchical FIFO queues . All in all, morphological segmentation techniques are computationally efficient, and there is no need to specify in advance the number of objects as with some Bayesian approaches. This is automatically accomplished by the marker or feature extraction step. However, by its very nature, the watershed algorithm suffers from the problems associated with other simple region-growing techniques.