+
+\subsection{Image Quality}
+The visual quality of the STABYLO scheme is evaluated in this section.
+Three metrics are computed in these experiments :
+the Peak Signal to Noise Ratio (PSNR),
+the PSNR-HVS-M~\cite{PSECAL07,psnrhvsm11} and the BIQI~\cite{MB10,biqi11}.
+The first one is widely used but does not take into
+account Human Visual System (HVS).
+The two last ones have been designed to tackle this problem.
+
+
+
+biqi = 28.3
+psnr-hvs-m= 78,6
+psnr-hvs= 67.3
+
+
\subsection{Steganalysis}
-Détailler \cite{Fillatre:2012:ASL:2333143.2333587}
-Vainqueur du BOSS challenge~\cite{DBLP:journals/tifs/KodovskyFH12}
+The quality of our approach has been evaluated through the two
+AUMP~\cite{Fillatre:2012:ASL:2333143.2333587}
+and Ensemble Classifier~\cite{DBLP:journals/tifs/KodovskyFH12} based steganalysers.
+Both aims at detecting hidden bits in grayscale natural images and are
+considered as the state of the art of steganalysers in spatial domain~\cite{FK12}.
+The former approach is based on a simplified parametric model of natural images.
+Parameters are firstly estimated and a adaptive Asymptotically Uniformly Most Powerful
+(AUMP) test is designed (theoretically and practically) to check whether
+a natural image has stego content or not.
+In the latter, the authors show that the
+machine learning step, (which is often
+implemented as support vector machine)
+can be a favourably executed thanks to an Ensemble Classifiers.
+
+
+
+\JFC{Raphael, il faut donner des résultats ici}
\ No newline at end of file
