Design Of Biorthogonal Filter Banks Research Articles

Design of Biorthogonal Filter Banks Using a Multi-objective Genetic Algorithm for an Image Coding Scheme

In this paper, we present an optimization method based on a multi-objective Genetic Algorithm (GA) for the design of linear phase filter banks for an image coding scheme. To be effective, the filter banks should satisfy a number of desirable criteria related to such a scheme. Instead of imposing the entire PR condition as in conventional designs, we introduce flexibility in the design by relaxing the Perfect Reconstruction (PR) condition and defining a PR violation measure as an objective criterion to maintain near perfect reconstruction (N-PR) filter banks. Particularly in this work, the designed filter banks are near-orthogonal. This has been made possible by minimizing the deviation from the orthogonality in the optimization process. The optimization problem is formulated as a constrained multi-objective, and a modified Nondominated Sorting Genetic Algorithm NSGAII is proposed in this work to find the Pareto optimal solutions that achieve the best compromise between the different objective criteria. The experimental results show that the filter banks designed with the proposed method outperform significantly the 9/7 filter bank of JPEG2000 in most cases. Furthermore, the filter banks are near orthogonal. This is very helpful, especially where embedded coding is required.

Two-stage, least squares design of biorthogonal filter banks

A two-stage approach is employed for the design of a class of two-channel biorthogonal filter banks. The filter banks belong to the class HPFB (halfband pair filter bank) and are defined by two kernels. The parametric Bernstein polynomial is used to construct the kernels. The design of the free parameters of the Bernstein polynomial is achieved through a least squares method. In the first stage, the analysis low-pass filter is designed and in the second stage, the synthesis low-pass filter is designed. With the two-stage approach, the design process is efficient and involves solving linear equations. The design technique allows filters with different characteristics to be designed easily.

Optimization design of biorthogonal filter banks for image compression

Abstract In this paper, we present a new approach for designing filter banks for image compression. This approach has two major components: optimization and generalization. In the optimization phase, we formulate the design problem as a nonlinear optimization problem whose objective consists of both the performance metrics of the image coder, such as the peak signal-to-noise ratio (PSNR), and those of individual filters. Filter banks are optimized in the optimization phase based on a set of training images. In the generalization phase, the filter bank that can be generalized to other images is selected from the candidates obtained in the optimization phase to be the final result. The filter bank selected should perform well not only on the training examples used in the design process but also on test cases not seen. In contrast to existing methods that design filter banks independently from the other operations in an image compression algorithm, our approach allows us to find filter banks that work best in a specific image compression algorithm for a certain class of images. In system prototype development, we adopt the agent-based approach to achieve better modularity, portability, and scalability. Agents in the multi-agent system are specialized in performing problem formulation, image compression, optimization, and generalization. In the experiments, we show that on a set of benchmark images our approach has found filter banks that perform better than the existing filter banks in different image compression algorithms and at different compression ratios.