Performance Analysis of Watermarking using Kronecker Product of Orthogonal Transforms and Wavelet Transforms

Abstarct— This paper proposes a digital image watermarking of still images using self-kronecker product of orthogonal transforms and Singular Value Decomposition. Singular Value Decomposition is used due to its high stability and high image energy compaction in few numbers of singular values. Kronecker product of two transforms helps us to focus on only global features and not the local features of an image. In proposed method, both these transforms are kept same to generate the transform matrix. 256x256 transform matrix is generated using kronecker product of two 16x16 matrices. Sinusoidal transforms DCT, DST, Real Fourier Transform, Sinecosine transform and non-sinusoidal transforms Walsh, Haar, Discrete Kekre Transform (DKT) and Slant transform are explored to generate transform matrix using self kronecker product. Using self kronecker product improves the performance against compression attack by 27-83%, against noise addition attack, 25% to 100 % and for resizing attack self kronecker product improves the robustness over self-wavelet transforms by 34-100%. For histogram equalization performance improvement from 5 to 34% is observed.