Information Hiding And Reversible Watermarking Algorithm Based On Vector Maps

With the popularity of computer networks and widespread networks, multimedia data can easily be obtained from the network, so the copyright protection,digital signatures and secure communication for multimedia data have been increasingly concerned by all the people.Information hiding and digital watermarking technology widely used in image,audio and video, as an effective copyright protection, secure communication tool, have been accepted by everyone. As an important branch of multimedia, the copyright protection of the vector maps have been drawn more and more attention by all the people. The algorithms of the copyright protection of vector maps come out one after the other. But the existing algorithm of watermark add the information at the cost of carriers, in the occasions of the high-precision, no doubt that the existing watermarking algorithms can not meet the requirements, the reversible watermarking for copyright protection in this situation is a kinds of good choices.This paper present the information hiding and watermark algorithms, while making the innovation and design like follows:Firstly, present the information hiding algorithm of RSDR based on vector maps, and then detailed design and implement of the algorithm. In the final tests of many vector maps, we get a good performance of the algorithm compared with the similar information hiding algorithm based on vector maps.Secondly, this paper proposes a new reversible watermarking method—interpolated prediction. Through a interpolated prediction way the authors obtain an approximate value of the existing value, and then embed the watermark by using the differential value relation between the approximate value and the existing value. During the extracting process, hiding information is firstly extracted, and the original image is recovered by using the agreement at the previous embedding step. At the basis of the algorithms research, this paper proposes the second kind of algorithms. Basically speaking, the second interpolated prediction algorithm is the revision and supplement version of the first algorithm, while the first algorithm still has a better embedding capacity in terms of the experimental data show. This is because the header information takes up most of the capacity of the embedded space, even if the more capacity in the new way, but this can be a breakthrough in the latter research. When comparing with other reversible algorithms in the same area, superior advantages of the presented algorithms in this paper are obvious.