Degree Granting Department
Computer Science and Engineering
Robin Murphy, Ph.D.
Miguel Labrador, Ph.D.
teleoperation, frame rate, bandwidth regulation, human factors, user contention
Robot and sensor networks are needed for safety, security, and rescue applicationssuch as port security and reconnaissance during a disaster. These applications rely on realtimetransmission of images, which generally saturate the available wireless networkinfrastructure. Knowledge-based Compression is a strategy for reducing the video frametransmission rate between robots or sensors and remote operators. Because images mayneed to be archived as evidence and/or distributed to multiple applications with differentpost processing needs, lossy compression schemes, such as MPEG, H.26x, etc., are notacceptable. This work proposes a lossless video server system consisting of three classesof filters (redundancy, task, and priority) which use different levels of knowledge (localsensed environment, human factors associated with a local task, and relative globalpriority of a task) at the application layer of the network. It demonstrates the redundancyand task filters for realistic robot search scenarios. The redundancy filter is shown toreduce the overall transmission bandwidth by 24.07% to 33.42%, and when combinedwith the task filter, reduces overall transmission bandwidth by 59.08% to 67.83%. Byitself, the task filter has the capability to reduce transmission bandwidth by 32.95% to33.78%. While Knowledge-based Compression generally does not reach the same levels ofreduction as MPEG, there are instances where the system outperforms MPEG encoding.
Scholar Commons Citation
Williams, Chris Williams, "Knowledge-Based Video Compression for Robots and Sensor Networks" (2006). Graduate Theses and Dissertations.