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.

This papers investigates linear binary wavelet transforms: convolutions of finite sequences of binary numbers with binary filters. These wavelets can be implemented using binary arithmetic only, which makes calculations simple and fast in practice. The paper presents some results that characterize binary wavelets completely for signal lengths that are powers of two. In addition, a design strategy is proposed that produces forward filters and inverse filters simultaneously by subsequently doubling the lengths of the filters. The paper concludes by investigating the effect of filter length on the compression of binary images.

Most research on wavelet transforms of binary signals and images has been concentrated on linear transforms. This paper proposes a binary wavelet transform scheme that does not depend on the field structure of binary numbers and includes nonlinear transforms. As a result, the number of binary wavelet transforms increases drastically. It is demonstrated that the increased freedom in choosing a particular transform can be useful in binary image compression. Nonlinear transforms reduce the empirical entropy of the test images substantially better than linear transforms.