Optimal differential energy watermarking of DCT encoded images and video.

This paper proposes the differential energy watermarking (DEW) algorithm for JPEG/MPEG streams. The DEW algorithm embeds label bits by selectively discarding high frequency discrete cosine transform (DCT) coefficients in certain image regions. The performance of the proposed watermarking algorithm is evaluated by the robustness of the watermark, the size of the watermark, and the visual degradation the watermark introduces. These performance factors are controlled by three parameters, namely the maximal coarseness of the quantizer used in pre-encoding, the number of DCT blocks used to embed a single watermark bit, and the lowest DCT coefficient that we permit to be discarded. We follow a rigorous approach to optimizing the performance and choosing the correct parameter settings by developing a statistical model for the watermarking algorithm. Using this model, we can derive the probability that a label bit cannot be embedded. The resulting model can be used, for instance, for maximizing the robustness against re-encoding and for selecting adequate error correcting codes for the label bit string.

Noise reduction of image sequences using motion compensation and signal decomposition.

In this paper, a new spatio-temporal filtering method for removing noise from image sequences is proposed. This method combines the use of motion compensation and signal decomposition to account for the effects of object motion. Because of object motion, image sequences are temporally nonstationary, which requires the use of adaptive filters. By motion compensating the sequence prior to filtering, nonstationarities, i.e., parts of the signal that are momentarily not stationary, can be reduced significantly. However, since not all nonstationarities can be accounted for by motion, a motion-compensated signal still contains nonstationarities. An adaptive algorithm based on order statistics is described that decomposes the motion-compensated signal into a noise-free nonstationary part and a noisy stationary part. An RLS filter is then used to filter the noise from the stationary signal. Our new method is experimentally compared with various noise filtering approaches from literature.

Spatially adaptive subsampling of image sequences.

In a spatially adaptive subsampling scheme, the subsampling lattice is adapted to the local spatial frequency content of an image sequence. In this paper, we use rate-distortion theory to show that spatially adaptive subsampling gives a better performance than subsampling with a fixed sampling lattice. A new algorithm that optimally assigns sampling lattices to different parts of the image is presented. The proposed spatially adaptive subsampling method can be applied within a motion-compensated coding scheme as well. Experiments show an increased performance over fixed lattice subsampling.