Abstract: A system and method for determining if a first image and a second image are correlated images includes partitioning a first image and a second image into a plurality of corresponding pixel partitions, calculating an average luminance value for each of the plurality of pixel partitions, determining if each of the plurality of pixel partitions of the first image is correlated with each of the corresponding plurality of pixel partitions of the second image, calculating a percentage of correlated pixel partitions of the first image and the corresponding plurality of pixel partitions of the second image and determining that the first image and the second image are correlated images if the percentage of correlated pixel partitions exceeds a predetermined pixel partition correlation threshold. The objective metric of the present invention determines whether two static rendered images are correlated enough to be undetectable by a human observer.

Abstract: A system and method for determining if a first image and a second image are correlated images includes partitioning a first image and a second image into a plurality of corresponding pixel partitions, calculating an average luminance value for each of the plurality of pixel partitions, determining if each of the plurality of pixel partitions of the first image is correlated with each of the corresponding plurality of pixel partitions of the second image, calculating a percentage of correlated pixel partitions of the first image and the corresponding plurality of pixel partitions of the second image and determining that the first image and the second image are correlated images if the percentage of correlated pixel partitions exceeds a predetermined pixel partition correlation threshold. The objective metric of the present invention determines whether two static rendered images are correlated enough to be undetectable by a human observer.

Abstract: The present invention provides a quantitative, automated system and method for assessing the correlation level of two rendered images, thereby removing subjectivity from such evaluation. The objective metric of the present invention determines whether two static images are correlated enough to be undetectable by a human observer. The performance of this method is optimized based upon the capabilities and limitations of the human visual system. Therefore, the resulting assessments are not overly sensitive and reduce the resources required to assess rendered images within a networked simulation environment. Additionally, the simplicity of the method lends itself to implementation within existing and emerging simulation systems with relatively little effort compared to current assessment methods. The system and method of the present invention provide benefits to multiple organizations, such as those engaged in human-in-the-loop simulators, distributed learning, and training applications.