Digital watermarking
Attack modelling: towards a second generation watermarking benchmark
Signal Processing - Special section on information theoretic aspects of digital watermarking
Better use of human visual model in watermarking based on linear prediction synthesis filter
IWDW'04 Proceedings of the Third international conference on Digital Watermarking
Spread-spectrum watermarking of audio signals
IEEE Transactions on Signal Processing
Image-adaptive watermarking using visual models
IEEE Journal on Selected Areas in Communications
Improved wavelet-based watermarking through pixel-wise masking
IEEE Transactions on Image Processing
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
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The two conflicting requirements of watermark invisibility and robustness are both required in most applications. The solution is to utilize a suitable perceptual quality metric (PQM) for watermarking correctly. This paper develops a new quality metric, the improved signal to noise ratio (iSNR). The improvement is done in the following two aspects: 1) SNR manifests much better performance in an image block of small size than in a whole image; 2) the average luminance and gradient information are added into SNR. Next, we propose a new adaptive watermarking framework based on the localized quality evaluation, which divides the cover data into nonoverlapping blocks and assigns an independent distortion constraint to each block to control the quality of it. In comparison with ones based on the global quality evaluation, the new one exploits the localized signal characteristics sufficiently while guaranteeing the localized watermark invisibility. Then, a specific implementation of the above framework is developed for image applying iSNR as the quality metric in the sense of maximizing the detection value. Experimental results demonstrate that the proposed watermarking performs very well both in robustness and invisibility.