Quantifying Interpretability for Motion Imagery: Applications to Image Chain Analysis
Charles D. Fenimore, J Irvine, D Cannon, S Israel, G O'Brien, A Aviles, John W. Roberts
The motion imagery community will benefit from the availability of standard measures for assessing image interpretability. The National Imagery Interpretability Rating Scale (NIIRS) has served as a community standard for still imagery, but no comparable scale exists for motion imagery. We conducted a series of user evaluations to understand and quantify the effects of critical factors affecting the perceived interpretability of motion imagery. These evaluations provide the basis for relating perceived image interpretability to image parameters, including ground sample distance and frame rate. The first section of this paper presents the key findings from these studies. The second portion illustrates the application of these methods to quantifying information loss due to compression of motion imagery. We conducted an evaluation of several methods for video compression (JPEG2000, MPEG2, and H.264) at various bitrates. A set of objective image quality metrics (peak SNR, an edge localization metric, and edge strength) were computed for the parent video clip and the various compressed products. In addition, we conducted an evaluation in which imagery analysts rated each clip relative to image interpretability tasks. The analysis quantifies the interpretability loss associated with the various compression methods and bitrates. Furthermore, it relates the objective image quality metrics to the user assessment of interpretability. The findings have implications for sensor system design, systems architecture, and missin planning.
Proceedings of the 10th International Conference on Information Fusion
July 9-12, 2007
10th International Conference on Information Fusion
, Irvine, J.
, Cannon, D.
, Israel, S.
, O'Brien, G.
, Aviles, A.
and Roberts, J.
Quantifying Interpretability for Motion Imagery: Applications to Image Chain Analysis, Proceedings of the 10th International Conference on Information Fusion, Quebec, CA, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=51144
(Accessed December 2, 2023)