Foveated coding for persistics

Abstract

Persistics is an advanced framework for processing wide-area aerial surveillance video. This framework handles the tasks of data collection, stitching of multi-sensor imagery, image registration and stabilization, motion tracking, and compression. As the technology for image sensor sizes improves, significant improvements in compression techniques are necessary in order to make full use of the data. Because the information of interest in such video is naturally moving, point-like targets, the applicability of foveated coding to the compression problem is an interesting question. Foveated coding, a compression technique that was designed to be perceptually optimal for the human visual system, has several components that are appropriate to the persistics compression problem. Foveation is applied in several different scenarios and methods to persistics data. As foveation can make good use of the persistics tracker data, a problem affecting tracker performance is explored as well. The multi-sensor stitching component of persistics can generate artifacts that reduce the effectiveness of the tracker. A method for characterizing, detecting, and correcting such artifacts is desirable. These three concepts are explored, and a method for detection is developed. Components of these algorithms were absorbed into a more general framework for artifact correction.