Variable bit rate video time-series and scene modeling using discrete-time statistically self-similar systems

This thesis investigates the application of discrete-time statistically self-similar (DTSS) systems to modeling of variable bit rate (VBR) video traffic data. The work is motivated by the fact that while VBR video has been characterized as self-similar by various researchers, models based on self-similarity considerations have not been previously studied. Given the relationship between self-similarity and long-range dependence the potential for using DTSS model in applications involving modeling of VBR MPEG video traffic data is presented. This thesis initially explores the characteristic properties of the model and then establishes relationships between the discrete-time self-similar model and fractional order transfer function systems. Using white noise as the input, the modeling approach is presented using least-square fitting technique of the output autocorrelations to the correlations of various VBR video trace sequences. This measure is used to compare the model performance with the performance of other existing models such as Markovian, long-range dependent and M/G/(infinity) . The study shows that using heavy-tailed inputs the output of these models can be used to match both the scene time-series correlations as well as scene density functions. Furthermore, the discrete-time self-similar model is applied to scene classification in VBR MPEG video to provide a demonstration of potential application of discrete-time self-similar models in modeling self-similar and long-range dependent data. Simulation results have shown that the proposed modeling technique is indeed a better approach than several earlier approaches and finds application is areas such as automatic scene classification, estimation of motion intensity and metadata generation for MPEG-7 applications

Sparse Signal Reconstruction using Weight Point Algorithm

In this paper we propose a new approach of the compressive sensing (CS) reconstruction problem based on a geometrical interpretation of l1-norm minimization. By taking a large l1-norm value at the initial step, the intersection of l1-norm and the constraint curves forms a convex polytope and by exploiting the fact that any convex combination of the polytope's vertexes gives a new point that has a smaller l1-norm, we are able to derive a new algorithm to solve the CS reconstruction problem. Compared to the greedy algorithm, this algorithm has better performance, especially in highly coherent environments. Compared to the convex optimization, the proposed algorithm has simpler computation requirements. We tested the capability of this algorithm in reconstructing a randomly down-sampled version of the Dow Jones Industrial Average (DJIA) index. The proposed algorithm achieved a good result but only works on real-valued signals