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A New Scalar Quantization Method for Digital Image Watermarking


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1 Computer Science, School of Information Sciences, University of Tampere, Kanslerinrinne 1, 33014 Tampere, Finland
 

A new technique utilizing Scalar Quantization is designed in this paper in order to be used for Digital Image Watermarking (DIW). Efficiency of the technique is measured in terms of distortions of the original image and robustness under different kinds of attacks, with particular focus on Additive White Gaussian Noise (AWGN) and Gain Attack (GA). The proposed technique performance is affirmed by comparing with state-of-the-art methods including Quantization Index Modulation (QIM), Distortion Compensated QIM (DC-QIM), and Rational Dither Modulation (RDM). Considerable improvements demonstrated by the method are due to a new form of distribution of quantized samples and a procedure that recovers a watermark after GA. In contrast to other known quantization methods, the detailed method here stipulates asymmetric distribution of quantized samples. This creates a distinctive feature and is expressed numerically by one of the proposed criteria. In addition, several realizations of quantization are considered and explained using a concept of Initial Data Loss (IDL) which helps to reduce watermarking distortions. The procedure for GA recovery exploits one of the two criteria of asymmetry. The accomplishments of the procedure are due to its simplicity, computational lightness, and sufficient precision of estimation of unknown gain factor.
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  • A New Scalar Quantization Method for Digital Image Watermarking

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Authors

Yevhen Zolotavkin
Computer Science, School of Information Sciences, University of Tampere, Kanslerinrinne 1, 33014 Tampere, Finland
Martti Juhola
Computer Science, School of Information Sciences, University of Tampere, Kanslerinrinne 1, 33014 Tampere, Finland

Abstract


A new technique utilizing Scalar Quantization is designed in this paper in order to be used for Digital Image Watermarking (DIW). Efficiency of the technique is measured in terms of distortions of the original image and robustness under different kinds of attacks, with particular focus on Additive White Gaussian Noise (AWGN) and Gain Attack (GA). The proposed technique performance is affirmed by comparing with state-of-the-art methods including Quantization Index Modulation (QIM), Distortion Compensated QIM (DC-QIM), and Rational Dither Modulation (RDM). Considerable improvements demonstrated by the method are due to a new form of distribution of quantized samples and a procedure that recovers a watermark after GA. In contrast to other known quantization methods, the detailed method here stipulates asymmetric distribution of quantized samples. This creates a distinctive feature and is expressed numerically by one of the proposed criteria. In addition, several realizations of quantization are considered and explained using a concept of Initial Data Loss (IDL) which helps to reduce watermarking distortions. The procedure for GA recovery exploits one of the two criteria of asymmetry. The accomplishments of the procedure are due to its simplicity, computational lightness, and sufficient precision of estimation of unknown gain factor.