+\subsection{Adaptive Embedding Rate}
+
+
+
+\subsection{Image Quality}
+The visual quality of the STABYLO scheme is evaluated in this section.
+Four metrics are computed in these experiments :
+the Peak Signal to Noise Ratio (PSNR),
+the PSNR-HVS-M familly~\cite{PSECAL07,psnrhvsm11} ,
+the BIQI~\cite{MB10,biqi11} and
+the weigthed PSNR (wPSNR)~\cite{DBLP:conf/ih/PereiraVMMP01}.
+The first one is widely used but does not take into
+account Human Visual System (HVS).
+The other last ones have been designed to tackle this problem.
+
+\begin{table}
+\begin{center}
+\begin{tabular}{|c|c|c|}
+\hline
+Embedding rate & Adaptive
+10 \% & \\
+\hline
+PSNR & 66.55 & 61.86 \\
+\hline
+PSNR-HVS-M & 78.6 & 72.9 \\
+\hline
+BIQI & 28.3 & 28.4 \\
+\hline
+wPSNR & 86.43& 77.47 \\
+\hline
+\end{tabular}
+\end{center}
+\caption{Quality measeures of our steganography approach\label{table:quality}}
+\end{table}
+
+
+Compare to the Edge Adpative scheme detailed in~\cite{Luo:2010:EAI:1824719.1824720}, our both wPSNR and PSNR values are always higher than their ones.
+
+\JFC{comparer aux autres approaches}
+
+
\subsection{Steganalysis}
machine learning step, (which is often
implemented as support vector machine)
can be a favourably executed thanks to an Ensemble Classifiers.
+
+
+
+\begin{table}
+\begin{center}
+\begin{tabular}{|c|c|c|c|}
+Shemes & \multicolumn{2}{|c|}{STABYLO} & HUGO\\
+\hline
+Embedding rate & Adaptive & 10 \% & 10 \%\\
+\hline
+AUMP & 0.39 & 0.22 & 0.50 \\
+\hline
+Ensemble Classifier & & & \\
+
+\hline
+\end{tabular}
+\end{center}
+\caption{Steganalysing STABYLO\label{table:steganalyse}}
+\end{table}
+
+
+\JFC{Raphael, il faut donner des résultats ici}
\ No newline at end of file
