A New Image Watermarking Method in Wavelet Domain Using Hessenberg and Grey Wolf Optimizer Decomposition
محورهای موضوعی : مهندسی هوشمند برق
1 - Department of Computer Engineering, Eram Higher Education Institute, Shiraz, Iran.
کلید واژه: Grey Wolf Optimizer, Invariant wavelet transform, Image watermarking, Optimization, Hessenberg decomposition.,
چکیده مقاله :
This paper proposes a novel robust image watermarking scheme in the wavelet domain based on Grey Wolf Optimizer (GWO) using Hessenberg decomposition. The proposed method consists of three new algorithms. The first new algorithm applies the redistributed invariant discrete wavelet transform (RIDWT) to the host image to obtain an invariant wavelet domain. Following the RIDWT transform, the low-frequency sub-band of wavelet transformed image is segmented into non-overlapping blocks. Subsequently, the most suitable blocks are selected using the sum of visual and edge entropies for the watermark embedding. In this algorithm, the watermark embedding process is performed by Hessenberg decomposition. In the second new algorithm, the watermark extraction process from the watermarked host image is performed using Hessenberg decomposition. Furthermore, the Grey Wolf Optimizer (GWO) is employed in the third proposed algorithm to obtain optimized threshold and compensation parameters. The proposed method in this paper was evaluated using two standard measures, NC and PSNR, and demonstrated significantly better performance than other methods. In addition to its simplicity, this method exhibits strong robustness against image variations and manipulations and substantially improves image quality.
This paper proposes a novel robust image watermarking scheme in the wavelet domain based on Grey Wolf Optimizer (GWO) using Hessenberg decomposition. The proposed method consists of three new algorithms. The first new algorithm applies the redistributed invariant discrete wavelet transform (RIDWT) to the host image to obtain an invariant wavelet domain. Following the RIDWT transform, the low-frequency sub-band of wavelet transformed image is segmented into non-overlapping blocks. Subsequently, the most suitable blocks are selected using the sum of visual and edge entropies for the watermark embedding. In this algorithm, the watermark embedding process is performed by Hessenberg decomposition. In the second new algorithm, the watermark extraction process from the watermarked host image is performed using Hessenberg decomposition. Furthermore, the Grey Wolf Optimizer (GWO) is employed in the third proposed algorithm to obtain optimized threshold and compensation parameters. The proposed method in this paper was evaluated using two standard measures, NC and PSNR, and demonstrated significantly better performance than other methods. In addition to its simplicity, this method exhibits strong robustness against image variations and manipulations and substantially improves image quality.
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