Consistency and Ambiguities of Quality No Reference Metric for Pansharpening

Ideally, evaluation of panchromatic and multispectral image fusion requires the use of a reference image, which is only available in a reduced scale protocol. Thus, no reference metrics such as the now standard Quality No Reference (QNR) were introduced. However, the QNR contains implicit implementation parameters which have not been studied yet. Using a statistical analysis based on rank correlation, we show that those parameters have a significant effect on the QNR values. Moreover, we extend previous results indicating that the QNR has low correlation with reference metrics at reduced scale. These results raise questions about the QNR’s relevance. They call for a standardization of implicit parameters so as to compare values across works, and for the QNR to only be seen as a complementary measure to reference metrics but not as a proxy. The developed protocol is also used to find the best set of implicit parameters, but could be generalized for the assessment of other no reference metrics. Finding such well behaved no reference metric is of critical interest for the development of unsupervised machine learning methods of pansharpening.