Assessing and comparing the performance of watermarking algorithms is difficult. The visibility of the watermark is an important aspect in this process. In this paper, we propose two metrics for evaluating the visual impact of video watermarks. Based on several different watermarking algorithms and video sequences, we identify the most prominent impairments as spatial noise and temporal flicker. We design the corresponding measurement algorithms and corroborate their performance through subjective experiments.
In this paper, we propose an original framework for an intuitive tuning of parameters in image and video segmentation algorithms. The proposed framework is very flexible and generic and does not depend on a specific segmentation algorithm, a particular evaluation metric, or a specific optimization approach, which are the three main components of its block diagram. This framework requires a manual segmentation input provided by a human operator as he/she would have performed intuitively. This input allows the framework to search for the optimal set of parameters which will provide results similar to those obtained by manual segmentation. On one hand, this allows researchers and designers to quickly and automatically find the best parameters in the segmentation algorithms they have developed. It helps them to better understand the degree of importance of each parameter's value on the final segmentation result. It also identifies the potential of the segmentation algorithm under study in terms of best possible performance level. On the other hand, users and
operators of systems with segmentation components, can efficiently
identify the optimal sets of parameters for different classes of images or video sequences. In a large extent, this optimization can be
performed without a deep understanding of the underlying algorithm,
which would facilitate the exploitations and optimizations in real
applications by non-experts in segmentation. A specific implementation
of the proposed framework was obtained by adopting a video segmentation algorithm invariant to shadows as segmentation component, a full reference segmentation quality metric based on a perceptually motivated spatial context, as the evaluation component, and a down-hill simplex method, as optimization component. Simulation results on various test sequences, covering a representative set of indoor and ourdoor video, show that optimal set of parameters can be obtained efficiently and largely improve the results obtained when compared to a simple implementation of the same segmentation algorithm with ad-hoc parameter setting strategy.
This paper proposes a method to embed information into a 3D model represented by a polygonal mesh. The approach used consists in slightly changing the position of the vertices, influencing the length of approximation of the normals to the surface. This technique exhibits relatively low complexity, and offers robustness to simple geometric tranformations. In addition, it does not introduce any visible distortion to the original model.