\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}



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.



\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}


