Quantification of image similarity is a common problem in image processing. For pairs of two images, a variety of options is available and well-understood. However, some applications such as dynamic imaging or serial sectioning involve the analysis of image sequences and thus require a simultaneous and unbiased comparison of many images. This paper proposes a new similarity measure, that takes a global perspective and involves all images at the same time. The key idea is to look at Schatten-q-norms of a matrix assembled from normalized gradient fields of the image sequence. In particular, for q= 0, the measure is minimized if the gradient information from the image sequence has a low rank. This global perspective of the novel S qN -measure does not only allow to register sequences from dynamic imaging, e.g. DCE-MRI, but is also a new opportunity to simultaneously register serial sections, e.g. in histology. In this way, an accumulation of small, local registration error s may be avoided. First numerical experiments show very promising results for a DCE-MRI sequence of a human kidney as well as for a set of serial sections. The global structure of the data used for registration with S qN is preserved in all cases.