Bandwidth Borrowing Schemes for Instantaneous Video-on-Demand Systems

A controlled multicast scheme provides instantaneous service, but limited server bandwidth causes some user requests to be either delayed or rejected when insufficient free bandwidth is available. Two borrowing schemes are proposed for instantaneous video-on-demand (VOD) that reduce the user request blocking rate by borrowing bandwidth from ongoing video streams when there is insufficient free bandwidth for the server to deliver a new video stream. Both these new schemes have proved to be successful in reducing blocking rate and increasing bandwidth utilization at the expense of temporarily degrading the video quality

TVL₁shape approximation from scattered 3D data

With the emergence in 3D sensors such as laser scanners and 3D reconstruction from cameras, large 3D point clouds can now be sampled from physical objects within a scene. The raw 3D samples delivered by these sensors however, contain only a limited degree of information about the environment the objects exist in, which means that further geometrical high-level modelling is essential. In addition, issues like sparse data measurements, noise, missing samples due to occlusion, and the inherently huge datasets involved in such representations makes this task extremely challenging. This paper addresses these issues by presenting a new 3D shape modelling framework for samples acquired from 3D sensor. Motivated by the success of nonlinear kernel-based approximation techniques in the statistics domain, existing methods using radial basis functions are applied to 3D object shape approximation. The task is framed as an optimization problem and is extended using non-smooth L1 total variation regularization. Appropriate convex energy functionals are constructed and solved by applying the Alternating Direction Method of Multipliers approach, which is then extended using Gauss-Seidel iterations. This significantly lowers the computational complexity involved in generating 3D shape from 3D samples, while both numerical and qualitative analysis confirms the superior shape modelling performance of this new framework compared with existing 3D shape reconstruction techniques

Fuzzy image segmentation of generic shaped clusters

The segmentation performance of any clustering algorithm is very sensitive to the features in an image, which ultimately restricts their generalisation capability. This limitation was the primary motivation in our investigation into using shape information to improve the generality of these algorithms. Fuzzy shape-based clustering techniques already consider ring and elliptical profiles in segmentation, though most real objects are neither ring nor elliptically shaped. This paper addresses this issue by introducing a new shape-based algorithm called fuzzy image segmentation of generic shaped clusters (FISG) that incorporates generic shape information into the framework of the fuzzy c-means (FCM) algorithm. Both qualitative and quantitative analyses confirm the superiority of FISG compared to other shape-based fuzzy clustering methods including, Gustafson-Kessel algorithm, ring-shaped, circular shell, c-ellipsoidal shells and elliptic ring-shaped clusters. The new algorithm has also been shown to be application independent so it can be applied in areas such as video object plane segmentation in MPEG-4 based coding

A novel approach to the design of DSP systems using minimum complexity Finite State Machines

The paper presents a new and different approach to the design and realisation of Digital Signal Processing (DSP)systems by utilising Finite State Machines (FSM). The DSP system is modelled by mapping all its potential states into an FSM, whose complexity is usually very high. The FSM mirrors the complete functionality of the system and thus describes its behaviour in full detail. Examples for FSMs of first and second order digital recursive filters are provided and the current version of the software simulating the FSM corresponding to any linear time-invariant DSP system is described. The potential of this approach including state reduction techniques as well as the inclusion of non-linear DSP systems is also outlined, and further future research intentions are briefly explored

Extended fuzzy rules for image segmentation

The generic fuzzy rule-based image segmentation (GFRIS) technique does not produce good results for non-homogeneous regions that possess abrupt changes in pixel intensity, because it fails to consider two important properties of perceptual grouping, namely surroundedness and connectedness. A new technique called extended fuzzy rules for image segmentation (EFRIS) is proposed, which includes a second rule to that defined already in GFRIS, that incorporates both the surroundedness and connectedness properties of a region's pixels. This additional rule is based on a split-and-merge algorithm and refines the output from the GFRIS technique. Two different classes of image, namely light intensity and medical X-rays are empirically used to assess the performance of the new technique. Quantitative evaluation of the performance of EFRIS is discussed and contrasted with GFRIS using one of the standard segmentation evaluation methods. Overall, EFRIS exhibits significantly improved results compared with the GFRIS approac

A generic fuzzy rule based technique for image segmentation

Many fuzzy clustering based techniques do not incorporate the spatial relationships of the pixels, while all fuzzy rule based image segmentation techniques tend to be very much application dependent. In most techniques, the structure of the membership functions are predefined and their parameters are either automatically or manually determined. This paper addresses the aforementioned problems by introducing a general fuzzy rule based image segmentation technique, which is application independent and can also incorporate the spatial relationships of the pixels. It also proposes the automatic defining of the structure of the membership functions. A qualitative comparison is made between the segmentation results using this method and the popular fuzzy c-means (FCM) applied to two types of images: light intensity (LI) and an X-ray of the human vocal tract. The results clearly show that this method exhibits significant improvements over FCM for both types of image

Enhanced cell visiting probability for QoS provisioning in mobile multimedia communications

This paper presents an enhanced cell visiting probability (CVP) estimation technique by integrating both mobility parameters such as position, direction, and speed together with exponential call duration probability of mobile units. These improved CVP estimates can be used in both adaptive and nonadaptive mobile networks to enhance QoS parameters. This paper also presents a new shadow-clustering scheme based on these enhanced CVPs, which is then applied to the call admission control scheme similar to the one, called predictive mobility support QoS provisioning scheme, proposed by Aljadhai and Znati (2001). Simulation results confirm that this new shadow-clustering scheme outperforms predictive mobility support QoS provisioning scheme in terms of different QoS parameters under various different traffic conditions

Feature weighting methods for abstract features applicable to motion based video indexing

Content based labels, associated with image sequences in contemporary video indexing methods, can be textual, numerical as well as abstract, including colour-histograms and motion co-occurrence matrices. Abstract features or indices are not explicitly numeric entities but rather are composed of numeric entities. When multiple abstract features are involved, distance metrics between image sequences need to be weighted. Most feature weighting methods in the literature assume that the space is numeric (either discrete or continuous) and so not applicable to abstract feature weighting. This paper elaborates some feature weighting methods applicable to abstract features and both binary (feature selection) and real-valued weighting methods are discussed. The performance of different feature selection and weighting methods are provided and a comparative study based on motion classification-experiments is presented

A new efficient similarity metric and generic computation strategy for pattern-based very low bit-rate video coding

In the context of very low bit-rate video coding, pattern representations of a moving region (MR) in block-based motion estimation and compensation has become increasingly attractive. Generally, all existing pattern-matching algorithms apply a similarity metric, involving elementary operations, to compute the mismatch between an MR and a particular fixed pattern in order to select the best-matching pattern from a fixed-size codebook of predefined patterns. An efficient similarity metric, together with a new generic computation strategy, is presented by considering only the mismatch areas of MRs. It is theoretically proven that for a specific MR in a macroblock, the new similarity metric selects exactly the same pattern as existing metrics, while the resulting computational coding efficiency is improved by between 21% and 58% compared with the H.263 low bit-rate coding standard

A real-time pattern selection algorithm for very low bit-rate video coding using relevance and similarity metrics

Very low bit-rate video coding using regularly shaped patterns to represent moving regions in macroblocks has good potential for improved coding efficiency. This paper presents a real-time pattern selection (RTPS) algorithm, which uses a pattern relevance and similarity metric to achieve faster pattern selection from a large codebook. For each applicable macroblock, the relevance metric is applied to create a customized pattern codebook (CPC) from which the best pattern is selected using the similarity metric. The CPC size is adapted to facilitate real-time selection. Results prove the quantitative and perceptual performance of RTPS is superior to both the Fixed-8 algorithm and H.263