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The transmission and storage of digital video currently requires more bandwidth than is typically available. Emerging applications such as video-on-demand, web cameras, and collaborative tools with video conferencing are pushing the limits of the transmission media to provide video to the desktop computer. Lossy compression has succeeded in meeting some of the video demand, but it suffers from artifacts and low resolution. This paper introduces a content-dependent, frame-selective compression technique which is developed wholly as a preconditioner that can be used with existing digital video compression techniques. The technique is heavily dependent on a priori knowledge of the general content of the video which uses content knowledge to make smart decisions concerning the frames selected for storage or transmission. The velocital information feature of each frame is calculated to determine the frames with the most active changes. The velocital information feature along with a priori knowledge of the application allows prioritization of the frames. Frames are assigned priority values with the higher priority frames being selected for transmission based on available bandwidth. The technique is demonstrated for two applications: an airborne surveillance application and a worldwide web camera application. The airborne surveillance application acquires digital infrared video of targets at a standard frame rate of 30 frames per second, but the imagery suffers from infrared sensor artifacts and spurious noise. The web camera application selects frames at a slow rate but suffers form artifacts due to lighting and reflections. The results of using content-dependent, frame-selective video compression shows improvement in image quality along with reduced transmission bandwidth requirements.
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