A filter banks design using a multiobjecive genetic algorithm for an image coding scheme
Abdelkader Boukhobza, Abdennacer Bounoua, A. Taleb Ahmed, Nasreddine Taleb · 2009
In this paper, we present a global optimisation method based on a multi-objective Genetic Algorithm (GA) for the design of filter banks in a lossy image coding scheme. To be effective, the filter banks should satisfy a number of desirable criteria related to such scheme. We formulate the optimization problem as multi-objective and we use the Non-dominated Sorting Genetic Algorithm approach (NSGAII) to solve this problem by searching solutions that achieve the best compromise between the different objectives criteria, these solutions are known as Pareto Optimal Solutions. Flexibility in the design is introduced by relaxing Perfect Reconstruction (PR) condition and defining a PR violation measure as an objective criterion to maintain near perfect reconstruction (N-PR) solutions. Furthermore, the optimized filter banks are near-orthogonal. This can only be made possible by minimizing the deviation from the orthogonality in the optimization process. Our designed filter banks lead to a significant improvement in performance of coding with respect to the 9/7 filter bank of JPEG2000 at high compression ratios and offer a slight improvement at low compression ratios.