QMF filter bank design by a new global optimization method

We study various global optimization methods for designing QMF (quadrature mirror filter) filter banks. We formulate the design problem as a nonlinear constrained optimization problem, using the reconstruction error as the objective, and other performance metrics as constraints. This formulation allows us to search for designs that are improvements over the best existing designs. We present NOVEL, a global optimization method for solving nonlinear continuous constrained optimization problems. We show that NOVEL finds better designs with respect to simulated annealing and genetic algorithms in solving QMF benchmark design problems. We also show that relaxing the constraints on transition bandwidth and stopband energy leads to significant improvements in the other performance measures