A Multiobjective Optimization Method for Designing M-Channel NPR Cosine Modulated Filter Bank for Image Compression

Anamika Jain, Aditya Goel

Department of Electronics and Communication Engineering, Maulana Azad National Institute of Technology, Bhopal, India.

DOI: 10.4236/eng.2015.72008                

Abstract

This paper proposes a method to design multichannel cosine modulated filter bank for image compression using multiobjective optimization technique. The design problem is a combination of stopband residual energy, least square error of the overall transfer function of the filter bank, coding gain with dc leakage free condition as constraint. The proposed algorithm uses Non-dominated Sorting Genetic Algorithm (NSGA) to minimize the mutually contradictory objective function by minimizing filter tap weights of prototype filter. The algorithm solves this problem by searching solutions that achieve the best compromise between the different objectives criteria. The performance of this algorithm is evaluated in terms of coding gain and peak signal to noise ratio (PSNR). Simulation results on different images are included to illustrate the effectiveness of the proposed algorithm for image compression application.

Keywords

Cosine Modulated Filter Bank, Near Perfect Reconstruction (NPR), Non-Dominated Sorting Genetic Algorithm (NSGA), Peak Signal to Noise Ratio (PSNR), Pseudo Quadrature Mirror Filter (PQMF) Bank

Conflicts of Interest

The authors declare no conflicts of interest.

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