The so-called texture loss is a critical parameter in the objective image quality assessment of todays cameras. Especially cameras build in mobile phones show significant loss of low contrast, fine details which are hard to describe using standard resolution measurement procedures. The combination of very small form factor and high pixel count leads to a high demand of noise reduction in the signal-processing pipeline of these cameras. Different work groups within ISO and IEEE are investigating methods to describe the texture loss with an objective method. The so-called dead leaves pattern has been used for quite a while in this context. Image Engineering presented a new intrinsic approach at the Electronic Imaging Conference 2014, which promises to solve the open issue of the original approach, which could be influenced by noise and artifacts. In this paper, we present our experience with the new approach for a large set of different imaging devices. We show, that some sharpening algorithm found in todays cameras can significantly influence the Spatial Frequency Response based on the Dead Leaves structure (SFRDeadLeaves) results and therefore make an objective evaluation of the perceived image quality even harder. For an objective comparison of cameras, the resulting SFR needs to be reduced to a small set of numbers, ideally a single number. The observed sharpening algorithms lead to much better numerical results, while the image quality already degrades due to strong sharpening. So the measured, high SFRDeadLeaves result is not wrong, as it reflects the artificially enhanced SFR, but the numerical result cannot be used as the only number to describe the image quality. We propose to combine the SFRDeadLeaves measurement with other SFR measurement procedures as described in ISO12233:2014. Based on the three different SFR functions using the dead leaves pattern, sinusoidal Siemens Stars and slanted edges, it is possible to obtain a much better description if the perceived image quality. We propose a combination of SFRDeadLeaves, SFREdge and SFRSiemens measurements for an in-depth test of cameras and present our experience based on todays cameras.