Spatio-temporal discrimination model predicting IR target detection

Many image discrimination models are available for static images. However, in many applications temporal information is important, so image fidelity metrics for image sequences are needed as well. Ahumada et al (1998)(1) presented a discrimination model for image sequences. It is unusual in that it does not decompose the images into multiple frequency and orientation channels. This helps make it computationally inexpensive. It was evaluated for predicting psychophysical experiments measuring contrast sensitivity and temporal masking. The results were promising. In this paper we investigate the performance of the above-mentioned model for a practical application - surveillance with infrared (IR) imagery. Model evaluation is based on two-alternative forced choice experiments, using a staircase procedure to control signal amplitude. The observer is presented with two one-second-duration IR-image sequences, one of which has an added target signal. The observer's task is to guess which sequence contained the target. While the target is stationary in the image centre, the background moves in one direction, simulating a tracking situation in which the observer has locked on to the target. The results shows that the model qualitatively, in four out of five cases, have the desired behaviour.