Reversible data embedding into images using wavelet techniques and sorting

The proliferation of digital information in our society has enticed a lot of research into data-embedding techniques that add information to digital content, like images, audio, and video. In this paper, we investigate high-capacity lossless data-embedding methods that allow one to embed large amounts of data into digital images (or video) in such a way that the original image can be reconstructed from the watermarked image. We present two new techniques: one based on least significant bit prediction and Sweldens´ lifting scheme and another that is an improvement of Tian´s technique of difference expansion. The new techniques are then compared with various existing embedding methods by looking at capacity-distortion behavior and capacity control.