Gaussian pixel weighting marks in amplitude modulation of color image watermarking

Enhanced images watermarking based on amplitude modulation

“…The watermark retrieval was achieved, without the need of original image, by using a prediction technique based on a linear combination of pixel values in a cross-shape neighbourhood around the embedded pixels. The method was further developed to improve its retrieval performance by introducing a Gaussian pixel weighing mask in the embedding process, and employing a linear combination of all nearby pixel values around the embedded pixels in the retrieval process [5]. Nevertheless, we found that if the number of watermark bits, 1 and -1, around the embedding pixels is not balanced, the probability to retrieve the watermark correctly will be decreased.…”

“…Then, based on the analysis results, we propose three different improving methods to enhance its performance, that is, by balancing the watermark bits around the embedding pixels in the watermark preparing process, by properly tuning the strength of watermark in the embedding process, and by modifying the linear combination of nearby pixel values around the embedded pixels in the retrieval process. After implementing the proposed methods in the watermarking scheme, we evaluate and compare the improved performance of our scheme with the previous schemes in [4] and [5].…”

Robust image watermarking based on luminance modification

“…Together with the new blind detection scheme based on an efficient prewhitening process and a correlation-based detector, their proposed mark exhibited impressive performance and the watermark capacity in the scheme was comparable to Ref. 15. However, the watermark embedding in the luminance component greatly degraded the perceptual quality of the watermarked image when compared with watermark embedding in the blue component at the same watermark strength.…”

“…where Y g ði; jÞ is the modified luminance value of the 3 × 3 pixel block obtained from the Gaussian pixel-weighting mask, 15 which is considered as an HVS-based tuning factor for watermark strength. In practice, s must be carefully selected to obtain the best trade-off between imperceptibility and robustness.…”

“…The extraction performance was improved by introducing a Gaussian pixel-weighting mask into the embedding process and employing a linear combination of all nearby pixel values around the embedded pixel. 15 However, if the number of watermark bits "1" and "−1" around the embedding pixel was not equal or balanced, 16 the summation of those watermark bits would result in a large value, which directly affects the accuracy of the original pixel prediction step in the watermark extraction process, and the probability of extracting the watermark correctly would decrease. Such circumstances can frequently occur when a watermark that is to be embedded consists of recognizable patterns.…”