Abstract
A hybrid watermarking scheme based on Triangular Vertex Transform (TVT) and Contourlet coefficients for high robustness is implemented. During watermark embedding, the cover image is first decomposed using Contourlet Transform to obtain high frequency and low frequency coefficients. The lower frequency coefficients are applied with TVT. Then, the W coefficients obtained from TVT are again subdivided. The watermark bit is then embedded on the subdivided coefficients to obtain the watermarked image. Reverse operation is followed in the extraction phase. The performance of this algorithm is evaluated using embedding capacity, Normalized cross correlation (Ncc) and Peak Signal to Noise Ratio (PSNR) using standard test images. These evaluation results disclose the domination of proposed scheme over traditional schemes
Highlights
Due to the development of high speed internet, huge number of media like images are created, manipulated, and shared through it especially in social media
The value of Structural Similarity Index Measurement (SSIM) and Peak Signal to Noise Ratio (PSNR) are highest for the test image Tiffany
The cover image is applied with Contourlet Transform (CT) to obtain the lower frequency coefficients which is again transformed using Triangular Vertex Transform (TVT)
Summary
Due to the development of high speed internet, huge number of media like images are created, manipulated, and shared through it especially in social media. It is very necessary to preserve the ownership of media like images. Digital image watermarking [1] provides a way to preserve the ownership of media, which have other applications in medical field and military. A good watermarking algorithm must have the properties like high embedding capacity, high visual quality and robustness against attacks. Several researchers are working in developing high capacity, high visual quality and highly robust algorithms. Spatial domain schemes [2] directly embed the data on the pixel intensities. Schemes on transform domain convert the pixels intensities into coefficients for embedding the data
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