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A Logarithmic Quantization-Based Image Watermarking Using Information Entropy in the Wavelet Domain
Conventional quantization-based watermarking may be easily estimated by averaging on a set of watermarked signals via uniform quantization approach. Moreover, the conventional quantization-based method neglects the visual perceptual characteristics of the host signal; thus, the perceptible distortio...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7512558/ https://www.ncbi.nlm.nih.gov/pubmed/33266669 http://dx.doi.org/10.3390/e20120945 |
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author | Liu, Jinhua Wu, Shan Xu, Xinye |
author_facet | Liu, Jinhua Wu, Shan Xu, Xinye |
author_sort | Liu, Jinhua |
collection | PubMed |
description | Conventional quantization-based watermarking may be easily estimated by averaging on a set of watermarked signals via uniform quantization approach. Moreover, the conventional quantization-based method neglects the visual perceptual characteristics of the host signal; thus, the perceptible distortions would be introduced in some parts of host signal. In this paper, inspired by the Watson’s entropy masking model and logarithmic quantization index modulation (LQIM), a logarithmic quantization-based image watermarking method is developed by using the wavelet transform. Furthermore, the novel method improves the robustness of watermarking based on a logarithmic quantization strategy, which embeds the watermark data into the image blocks with high entropy value. The main significance of this work is that the trade-off between invisibility and robustness is simply addressed by using the logarithmic quantizaiton approach, which applies the entropy masking model and distortion-compensated scheme to develop a watermark embedding method. In this manner, the optimal quantization parameter obtained by minimizing the quantization distortion function effectively controls the watermark strength. In terms of watermark decoding, we model the wavelet coefficients of image by the generalized Gaussian distribution (GGD) and calculate the bit error probability of proposed method. Performance of the proposed method is analyzed and verified by simulation on real images. Experimental results demonstrate that the proposed method has the advantages of imperceptibility and strong robustness against attacks covering JPEG compression, additive white Gaussian noise (AWGN), Gaussian filtering, Salt&Peppers noise, scaling and rotation attack, etc. |
format | Online Article Text |
id | pubmed-7512558 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75125582020-11-09 A Logarithmic Quantization-Based Image Watermarking Using Information Entropy in the Wavelet Domain Liu, Jinhua Wu, Shan Xu, Xinye Entropy (Basel) Article Conventional quantization-based watermarking may be easily estimated by averaging on a set of watermarked signals via uniform quantization approach. Moreover, the conventional quantization-based method neglects the visual perceptual characteristics of the host signal; thus, the perceptible distortions would be introduced in some parts of host signal. In this paper, inspired by the Watson’s entropy masking model and logarithmic quantization index modulation (LQIM), a logarithmic quantization-based image watermarking method is developed by using the wavelet transform. Furthermore, the novel method improves the robustness of watermarking based on a logarithmic quantization strategy, which embeds the watermark data into the image blocks with high entropy value. The main significance of this work is that the trade-off between invisibility and robustness is simply addressed by using the logarithmic quantizaiton approach, which applies the entropy masking model and distortion-compensated scheme to develop a watermark embedding method. In this manner, the optimal quantization parameter obtained by minimizing the quantization distortion function effectively controls the watermark strength. In terms of watermark decoding, we model the wavelet coefficients of image by the generalized Gaussian distribution (GGD) and calculate the bit error probability of proposed method. Performance of the proposed method is analyzed and verified by simulation on real images. Experimental results demonstrate that the proposed method has the advantages of imperceptibility and strong robustness against attacks covering JPEG compression, additive white Gaussian noise (AWGN), Gaussian filtering, Salt&Peppers noise, scaling and rotation attack, etc. MDPI 2018-12-08 /pmc/articles/PMC7512558/ /pubmed/33266669 http://dx.doi.org/10.3390/e20120945 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Liu, Jinhua Wu, Shan Xu, Xinye A Logarithmic Quantization-Based Image Watermarking Using Information Entropy in the Wavelet Domain |
title | A Logarithmic Quantization-Based Image Watermarking Using Information Entropy in the Wavelet Domain |
title_full | A Logarithmic Quantization-Based Image Watermarking Using Information Entropy in the Wavelet Domain |
title_fullStr | A Logarithmic Quantization-Based Image Watermarking Using Information Entropy in the Wavelet Domain |
title_full_unstemmed | A Logarithmic Quantization-Based Image Watermarking Using Information Entropy in the Wavelet Domain |
title_short | A Logarithmic Quantization-Based Image Watermarking Using Information Entropy in the Wavelet Domain |
title_sort | logarithmic quantization-based image watermarking using information entropy in the wavelet domain |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7512558/ https://www.ncbi.nlm.nih.gov/pubmed/33266669 http://dx.doi.org/10.3390/e20120945 |
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