Multi-Objective Genetic Algorithm Optimization for Image Watermarking Based on Singular Value Decomposition and Lifting Wavelet Transform

Abstract

In this paper, a new optimal watermarking scheme based on singular value decomposition (SVD) and lifting wavelet transform (LWT) using multi-objective genetic algorithm optimization (MOGAO) is presented. The singular values of the watermark is embedded in a detail subband of host image. To achieve the highest possible robustness without losing watermark transparency, multiple scaling factors (MSF) are used instead of single scaling factor (SSF). Determining the optimal values of the MSFs is a difficult problem. However, to find this values a multi-objective genetic algorithm optimization is used. Experimental results show a much improved performance in term of transparency and robustness of the proposed method compared to others methods.

Based Image Watermarking Algorithm Based on Qr Decomposition in Lifting Wavelet

“…The performance of this scheme is better than DWT (in terms of time and space) therefore it is being widely used in image processing (i.e. image compression [7], watermarking [14] [18]). The signal decomposition in LWT is achieved through following three steps:…”

Case study on wavelet choice based on statistical image quality measures

“…A multiobjective approach is also used in image fusion [3,9], watermarking [10,11], and image/video coding [4,12].…”

“…The proposed algorithm can be summarized as follows: select the proper thresholding function considering entropy, standard deviation (SD), peak signal to noise ratio (PSNR), and structural similarity (SSIM); perform a 2D discrete wavelet transform (DWT); optimize thresholds using adaptive multiobjective particle swarm optimization (AMOPSO); threshold the coefficients of detailed coefficients by Pareto optimal threshold; and reconstruct the image. A multiobjective approach is also used in image fusion [3,9], watermarking [10,11], and image/video coding [4,12].…”

“…An energy algorithm based on wavelets is also used in [3] for image fusion, but with multiobjective optimization. Watermarking is usually based on singular value decomposition (SVD) in the wavelet domain with combining the GA and multiobjective optimization [10,11].…”