Image quality assessment have been extensively studied during this past few decades. It is obviously very important to provide a mean to judge an image's quality without having to ask to human observers for a sub jective image quality evaluation. Many computer softwares have been build in this aim. This is called ob jective quality assessment. Such metrics are usually of three kinds, they may be Full Reference (FR), Reduced Reference (RR) or No Reference (NR) metrics. We focus here on a new technique which recently appeared in quality assessment metrics: data-hiding-based image quality metric. Regarding the amount of data to be transmitted for quality assessment purpose, this latter is placed in between RR and NR metrics. A little overhead due to the embedded watermark is added to the image. A perceptually weighted watermark is embedded into the host image, and an evaluation of this watermark leads to assess the host image's quality. In such context, the watermark robustness is crucial. The watermark must resist to most attacks, but it must also be degraded along with the image distortion. Our work is compared to existing metrics in terms of the correlation (et de RMSE ?) with sub jective assessment and in terms of data overhead induced by the mark.