One-pass lossless data hiding and compression of remote sensing data

In this paper, we introduce a one-pass framework that is able to perform the lossless data hiding and the lossless compression of the marked stream, at the same time, by exploiting the capabilities of the predictive paradigm. Substantially, in a single pass, a marked and compressed stego image is obtained, which can be exactly restored by the receiver: by decompressing and reversibly reconstructing the original unaltered image. In addition, our framework also permits to perform only the decompression (without the extraction of the hidden information). In this manner, the resulting stego (marked) hyperspectral image, could be used for several purposes, in which it is not necessary to extract the original data and an acceptable grade of degradation is tolerated. We also implement a proof-of-concept of the proposed framework to assess the effectiveness of our contribution. Finally, we report the achieved experimental results, which outperform other similar approaches.