The wide use of cameras by the public has raised the interest of image quality evaluation and ranking. Current cameras embed complex processing pipelines that adapt strongly to the scene content by implementing, for instance, advanced noise reduction or local adjustment on faces. However, current methods of Image Quality assessment are based on static geometric charts which are not representative of the common camera usage that targets mostly portraits. Moreover, on non-synthetic content most relevant features such as detail preservation or noisiness are often un-tractable. To overcome this situation, we propose to mix classical measurements and Machine learning based methods: we reproduce realistic content triggering this complex processing pipelines in controlled conditions in the lab which allows for rigorous quality assessment. Then, ML based methods can reproduce perceptual quality annotated previously. In this paper, we focus on noise quality evaluation and test on two different set ups: closeup and distant portraits. These setups provide scene capture conditions flexibility, but most of all, allow the evaluation of all quality camera ranges from high quality DSLR to poor quality video conference. Our numerical results show the relevance of our solution compared to geometric charts and the importance of adapting to realistic content.