X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/canny.git/blobdiff_plain/ded436ff1397fd455304cbae04f58e4c0703ea01..100ac9908382b8de480dae1fd68829f2a7778b5d:/experiments.tex?ds=inline

diff --git a/experiments.tex b/experiments.tex
index 60b0f2f..a79b1d4 100644
--- a/experiments.tex
+++ b/experiments.tex
@@ -1,6 +1,38 @@
+
+\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