A Reversible Watermarking Algorithm Based on Block Adaptive Compressed Sensing

To balance high embedding capacity and imperceptibility of reversible watermarking algorithm for digital images, a novel Reversible Watermarking Algorithm based on Block Adaptive Compressed Sensing (BACS-RWA) is proposed. The host image is divided into blocks and the types of these blocks are determined with the statistical relationship between the surrounding image blocks and the target block. The capacity parameters are adaptively selected to do block compressed sensing and the watermarking is embedded with integer transformation. In order to improve embedding capacity, the smooth and normal blocks of compressed sensing host image are used to embed watermarking. Complex blocks are not processed to insure image quality and imperceptibility. Reconstruction algorithm of block compressed sensing and reversible integer transformation are used to reconstruct the host image accurately. Simulation of this algorithm is performed on different texture images and compared with similar algorithms. Experimental results show that the optimal embedding capacity can reach up to 1.87 bpp when Plane is used as host image. The introduction of block adaptive compressed sensing theory leads to better comprehensive performance. It can not only improve embedding capacity, but also reduce effectively the influence of embedding data on the quality of the host image.