Robot and sensor networks are needed for safety, security, and rescue applications such as port security and
reconnaissance during a disaster. These applications rely on real-time transmission of images, which generally saturate the
available wireless network infrastructure. Knowledge-based compression is a method for reducing the video frame
transmission rate between robots or sensors and remote operators. Because images may need to be archived as evidence
and/or distributed to multiple applications with different post processing needs, lossy compression schemes, such as MPEG,
H.26x, etc., are not acceptable. This work proposes a lossless video server system consisting of three classes of filters
(redundancy, task, and priority) which use different levels of knowledge (local sensed environment, human factors associated
with a local task, and relative global priority of a task) at the application layer of the network. It demonstrates the
redundancy and task filters for a realistic robot search scenario. The redundancy filter is shown to reduce the overall
transmission bandwidth by 24.07% to 33.42%, and, when combined with the task filter, reduces overall transmission
bandwidth by 59.08%to 67.83%. By itself, the task filter has the capability to reduce transmission bandwidth by 32.95% to
33.78%. While knowledge-based compression generally does not reach the same levels of reduction as MPEG, there are
instances where the system outperforms MPEG encoding.
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