Patrick D. O'Shea U.S. Army Redstone Technical Test Ctr. (United States) Eddie L. Jacobs The Univ. of Memphis (United States) Richard Lazarte Espinola U.S. Army Night Vision & Electronic Sensors Directorate (United States)
As the number of fielded sensors proliferates, sensors are being implemented in sensor networks with wired or wireless exchange of information. To handle the expanding load of data with limited network bandwidth resources, both still and moving imagery can be highly compressed. However, high levels of compression are not error-free, and the resulting images contain artifacts that may adversely affect the ability of observers to detect or identify targets of interest. This paper attempts to quantify the effect of image compression on observer tasks such as target identification. We addressed two typical compression algorithms, at two levels of compression, in a series of controlled perception experiments to isolate and quantify the effects on observer task performance. We find that the performance loss caused by image compression is well modeled by the use of an effective per-pixel blur, and give those blurs for the cases we used.