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 technique, really 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 suite of content-based functionalities, specifically, content-based interactivity, compression and common entry. notwithstanding, 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 makes a speciality of the categorical factor of face segmentation and coding as utilized to videoconferencing with the intention to enhance the standard of videoconferencing photos specially within the facial sector.
Modal-based coding is a content-based coding approach used to code artificial items that experience develop into a big a part of video content material. It ends up in super low bit premiums simply because merely the parameters had to symbolize the modal are transmitted. Model-based coding is integrated to supply heritage details for the bogus item coding in MPEG-4. finally, MPEG-4, the 1st coding typical for multimedia content material is defined intimately. the subjects lined comprise the coding of audio gadgets, the coding of traditional and artificial video gadgets, and blunder resilience.
Advanced Video Coding is likely one of the first books on content-based coding and MPEG-4 coding common. It serves as a good details resource and reference for either researchers and practising engineers.
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Extra resources for Advanced Video Coding: Principles and Techniques, Volume 7: The Content-based Approach
On the other hand, the rather generic assumption of smoothness makes non-parametric methods applicable for a broad range of situations and applications. Non-parametric dense field representations are, however, not directly suitable for segmentation. Apart from the simple case of pure translation, an object moving in 3-D space generates a spatially varying 2-D motion field even within the same object. Hence, it would be difficult to group pixels based on the similarity of their flow vectors. For that reason, parametric models are commonly used in segmentation algorithms.
Another advantage of parametric representations is that they are less sensitive to noise because many pixels contribute to the estimation of a few parameters. Furthermore, there is no blurring of motion boundaries as long as they coincide with region boundaries. The necessity of a segmentation and some possibly restrictive assumptions on the scene and motion are among the drawbacks of parametric representations. Note that the requirements on the segmentation here are not the same as for VOP extraction.
Therefore, we can set T - 1 and 2or2 - 1 to simplify the expression. This results in the following cost or objective function to be minimized with respect to X(i,j): Cost(X(i,j)) - ~ Vc(x) + (o(i,j) - #x(i,j)) 2 . 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.