Research On Optimized Block Truncation Coding And Data Hiding Algorithm Based On Compressed Images

With the rapid development and application of the Internet, digital images are transmitted extensively and used in many fields. This causes new requirements for the processing of digital images. On one hand, digital images often contain a significant amount of data, which poses high challenge on the storage and transmission of digital images. Thus, it is necessary to compress these images before they are stored and transmitted. The goal of compressing image is to remove redundancies in the image so it can be represented by a smaller number of bits with little, if any, degradation in the quality of the image.On the other hand, data hiding techniques are required when digital images are used to embed secret information in order to protect the important information.Considering these two aspects, data hiding for compressed images becomes an important research in image transmission field. Therefore, our paper is focused on data hiding techniques for compressed images and we mainly make a study of block truncation coding and data hiding techniques based on particle swarm optimization. Our work is described as follows.First, we propose the optimized block truncation coding method for digital images. Block truncation coding is famous for its efficiency because it has low computation complexity. Moreover, it can maintain a good visual quality of the restored image. This paper propose two schemes based on block truncation coding, one is to optimize the pixel grouping for grayscale images by using particle swarm optimization, another is to optimize the common bitmap for color images by using binary ant colony optimization. Compared with the related schemes, our schemes can achieve better visual quality.Second, we propose the optimized data hiding schemes based on particle swarm optimization. In this paper, we propose two schemes: the first scheme optimizes the turtle shell matrix by using particle swarm optimization. With the same embedding capacity, our scheme minimizes the turtle-shell matrix based stego-image distortion. The second one optimizes a data hiding scheme for compressed grayscale images by combining block truncation coding and particleswarm optimization. This scheme achieves a large embedding capacity and good visual quality of the stego-image.