中文摘要：

针对数字图像可逆水印的高嵌入容量和不可见性的权衡问题，该文提出一种基于分块自适应压缩感知的可逆水印算法（ReversibleWatermarkingAlgorithmBasedonBlockAdaptiveCompressedSensing．BACS—RWAl。该算法对载体图像分块，利用周围块与目标块的统计关系判断块类型，自适应地选择容量参数进行分块压缩感知，并利用整数变换嵌入水印；为提高水印嵌入容量将水印嵌入到经压缩感知后的平滑和普通载体图像块中，复杂载体图像块不做处理，以确保图像质量和不可感知性；采用分块压缩重构算法和可逆整数变换来恢复载体图像。通过对不同纹理图像实验并与同类算法对比，结果表明：当以Plane为载体图像时，最佳嵌入容量达1．87bpp。分块白适应压缩感知理论的引入使算法具有良好的综合性能，在提高嵌入容量的同时，又能有效地降低嵌入数据后对原始图像质量的影响。

英文摘要：

To balance high embedding capacity and imperceptibility of reversible watermarking atgomtnm ior 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.