Diffuse prior monotonic likelihood ratio test for evaluation of fused image quality metrics

This paper introduces a novel method to score how well proposed fused image quality measures (FIQMs) indicate the effectiveness of humans to detect targets of interest in fused imagery. The human detection performance is measured via human perception experiments. A good FIQM should relate to perception results in a monotonic fashion. The new method, the diffuse prior monotonic likelihood ratio (DPMLR) test, compares the H1 hypothesis that the intrinsic human detection performance is related to the FIQM via a monotonic function to the null hypothesis that the detection and image quality relationship is random. The paper discusses many interesting properties of the DPMLR and demonstrates the effectiveness of the DPMLR test via Monte Carlo Simulations. Finally, the DPMLR is used to score FIQMs over 35 scenes implementing various image fusion algorithms.