This survey introduces the current state of the art in image and video retargeting and describes important ideas and technologies that have influenced the recent work. Retargeting is the process of adapting an image or video from one screen resolution to another to fit different displays, for example, when watching a wide screen movie on a normal television screen or a mobile device. As there has been considerable work done in this field already, this survey provides an overview of the techniques. It is meant to be a starting point for new research in the field. We include explanations of basic terms and operators, as well as the basic workflow of the different methods.

In this paper, a novel warping-based retargeting approach for stereoscopic video is proposed. It considers the three conflicting goals: preserving salient image content, avoiding flickering and maintaining consistency between the two views. The first step of the approach focuses on content-aware image resizing and considers image saliency, motion saliency, and depth saliency. In the second step, temporal coherence is preserved by tracking and optimizing deformed pathlines. The proposed algorithm maps the mesh from the left to the right view to guarantee consistency between the deformation of objects between the views. Users rated the quality of the adapted videos as good. In particular, distortions in the perceived depth are not noticeable, and the temporal stability is significantly higher compared to seam carving approaches.

In this paper, we present a fast parallel algorithm for the retargeting of videos. It combines seam carving and cropping and is aimed for real-time adaptation of video streams. The basic idea is to first find an optimal cropping path over the whole sequence with the target size. Then, the borders are slightly extended to be reduced again by seam carving on a frame-by-frame basis. This allows the algorithm to get more important content into the cropping window as it is also able to remove pixels from within the window. In contrast to the previous SeamCrop algorithm, the presented technique is optimized for parallel processes and a CUDA GPU implementation. In comparison, the computation time of our GPU algorithm is 10.5 times faster (on a 960 x 540 video with a retarget factor of 25%) than the already efficient CPU implementation.

We present a novel system that allows small handheld devices to detect other devices in the local proximity and to exchange messages with them. The client devices use WLAN to access the system via a web interface and do not require additional software or modification of the device's operating system. The physical closeness of client devices is estimated based on their signal strengths measured by the system's access points. A major advantage compared to indoor positioning systems is the fact that our system does not require a training phase. We conduct a comprehensive evaluation to demonstrate the practicability of our approach.

The presentation of multimedia data and especially of high resolution videos on small mobile devices is still a great challenge today. Both cropping of borders and scaling of frames may result in the removal of essential content of videos or lost details due to the reduced size of the visual content. Another major problem emerges if the aspect ratio of the original video and the display of the mobile device differ. User evaluations indicate that changing the aspect ratio may reduce the visual quality of videos significantly. In this paper, we present the new FSCAV algorithm (Fast Seam Carving for Size Adaptation of Videos) to adapt the size of videos to the limited display resolution and different aspect ratios of handheld mobile devices. The general idea of the seam carving algorithm for still images is to remove seams in images so that the essential content is preserved. We extended this technique which works very well for images to create videos without jitter or visible artifacts. A major feature of our FSCAV algorithm is the low computational complexity which enables an efficient adaptation of videos to small screens. Nevertheless, severe distortions are clearly visible in some shots of the adapted videos. We present a new heuristic to identify shots with such a low visual quality. If the quality drops below a threshold, a different adaptation technique is used for this shot (e.g., scaling or cropping). User evaluations confirm a very high visual quality of our approach.