Current television systems cope with a gap between broadcast and display resolution. Currently, scaling together with high-end sharpening techniques is considered state of the art in consumer products. For larger resolutions and more detail we propose Super-Resolution, a known technique, but with several new insights. Such as Similarity Adjustment combined with Rejection to provide a higher accuracy. Furthermore, Temporal Nearest First, a Multi-Frame Motion Estimator that even more increases accuracy so more detail can be obtained. The latter also makes an implementation more feasible in a television application.

Media storage devices experience browsing difficulty for users. One cause is the rapid growth in Digital Photography that results in a large database of stored Digital Photographs. Managing these photographs manually becomes a complex task. Hence there is a need to organize the Digital Photographs automatically. The application proposed in this paper solves this by using algorithms for image understanding and techniques for interpreting the metadata present in the Digital Photographs. Using the algorithms and the metadata, the application organizes the images based on People, Event, Location and semantic meaning. Hence the application provides an intuitive way for organizing and browsing Digital Photographs automatically.

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