Although the fusion of Panchromatic(PAN) images and Multi-spectral(MS) images can improve the resolution of remote sensing MS images, the joint adjustments of PAN images and MS images will lead to the original data being completely lost. With the requirement of on-orbit image processing and multiple applications, obtaining fusion images while retaining the source images has become a problem that must be considered. To deal with the problem that the original data is lost after fusion, this proposed method embeds the PAN image object and compression data into the fused image. The experimental results show that the proposed method can reconstruct the object data of the PAN image losslessly and reconstruct the entire PAN image in high quality. The fused image indicators also have no impact.
Reversible data hiding algorithms based on prediction error histogram of rhombus prediction need excellent prediction performance to achieve more embedding capacity. However, the cost of improving the prediction accuracy is to reduce the number of prediction pixels, which results in a reduction in embedding capacity. It means high prediction accuracy and large embedding capacity become two contradictory conditions. In this paper, we proposed a reversible data hiding algorithm based on improved rhombus prediction, which can break through the upper limit of prediction pixel numbers and increase the embedding capacity. The proposed algorithm utilizes the characteristics of reversible data hiding process to carry out the second stage reversible embedding based on the traditional rhombus prediction. It can embed the additional data into the context pixels of the first stage rhombus prediction so that the embedding capacity will be increased significantly. At the same time, reusing the reversible data hiding characteristics can ensure the lossless recovery of the carrier image and secret information. Experiments show that the algorithm can improve embedding capacity significantly and ensure the high visual performance of marked images.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.