Showing papers by "M. Omair Ahmad published in 2002"

Weighted least-square design of FIR filters using a fast iterative matrix inversion algorithm

Abstract: It has been shown by some researchers that in a problem of weighted least-square (WLS) design of finite-impulse response (FIR) filters, bulk of the design computation is concerned with the evaluation of the inverse of a matrix in order to solve a system of equations. In this paper, a new algorithm for the WLS design of FIR filters is presented, in which an iterative procedure is developed for the inversion of the matrix involved in the design. By imposing a mild constraint on the updation factor of the weighting function, the inverse of a matrix is expanded as a convergent power series. By investigating the properties of some of the matrices from the design formulation, a modified version of the series that converges rapidly is then proposed to evaluate the inverse in each iteration. It is shown that due to the fast convergence of the power series, one needs to evaluate only the first two or three terms of the series except during the initial stages of the iterations, implying that the conventional operation for matrix inversion is simplified significantly.

Wavelet-based multiresolution motion estimation through median filtering

Abstract: In this paper, a non-causal median filtering method is proposed to predict the motion vectors across the wavelet subbands of a video frame for multiresolution motion estimation. This median filtering method effectively overcomes the problem of propagation of false motion vectors that exists in the conventional multiresolution motion estimation schemes. A significant feature of the proposed technique is that it imposes no demand for additional bandwidth. Simulation studies show that this median filtering-based multiresolution motion; estimation technique effectively improves the motion prediction performance. It is further shown that this performance improvement is achieved with little increase in the computational complexity.

A neighborhood-blocks motion estimation technique using the pyramidal data structure

Abstract: In this paper, a pyramidal motion estimation technique that makes use of the motion correlation within a pyramidal level is proposed. Instead of scaling the motion vectors from the adjacent lower pyramidal level as motion predictions as is done in the conventional technique, in the proposed technique, the motion vectors from the neighboring motion blocks are taken into consideration as possible candidates. Each of these candidate motion vectors is used for prediction and refined. The motion vector that has the least matching distortion is chosen as the final motion vector. Compared to the conventional pyramidal motion estimation technique, the proposed method effectively overcomes the problem of propagation of false motion vectors. Simulation studies show that a substantial performance improvement is achieved, both in terms of the prediction mean square error and the number of coding bits for the motion vectors.