debut d'experiment

diff --git a/biblio.bib b/biblio.bib
index 8e5c9dec11f5a393f2a1780cff2bbce15454d326..56805804cd011630cc15befd5fc2642fd9842338 100644 (file)
--- a/biblio.bib
+++ b/biblio.bib
@@ -1,3 +1,30 @@
+@inproceedings{DBLP:conf/ih/PereiraVMMP01,
+  author    = {Shelby Pereira and
+               Sviatoslav Voloshynovskiy and
+               Maribel Madueno and
+               St{\'e}phane Marchand-Maillet and
+               Thierry Pun},
+  title     = {Second Generation Benchmarking and Application Oriented
+               Evaluation},
+  booktitle = {Information Hiding},
+  year      = {2001},
+  pages     = {340-353},
+  ee        = {http://dx.doi.org/10.1007/3-540-45496-9_25},
+  crossref  = {DBLP:conf/ih/2001},
+  bibsource = {DBLP, http://dblp.uni-trier.de}
+}
+@proceedings{DBLP:conf/ih/2001,
+  editor    = {Ira S. Moskowitz},
+  title     = {Information Hiding, 4th International Workshop, IHW 2001,
+               Pittsburgh, PA, USA, April 25-27, 2001, Proceedings},
+  booktitle = {Information Hiding},
+  publisher = {Springer},
+  series    = {Lecture Notes in Computer Science},
+  volume    = {2137},
+  year      = {2001},
+  isbn      = {3-540-42733-3},
+  bibsource = {DBLP, http://dblp.uni-trier.de}
+}

 @article{Hu:2007:HPE:1282866.1282944,
  author = {Hu, Liming and Cheng, H. D. and Zhang, Ming},
  title = {A high performance edge detector based on fuzzy inference rules},
 @article{Hu:2007:HPE:1282866.1282944,
  author = {Hu, Liming and Cheng, H. D. and Zhang, Ming},
  title = {A high performance edge detector based on fuzzy inference rules},

diff --git a/experiments.tex b/experiments.tex
index a79b1d468233f32001e17a8b92ff6710364ca046..40cd93b07043aa31cf3a59fded05af3b943afda4 100644 (file)
--- a/experiments.tex
+++ b/experiments.tex
@@ -1,18 +1,39 @@
 
 \subsection{Image Quality}
 The visual quality of the STABYLO scheme is evaluated in this section.
 
 \subsection{Image Quality}
 The visual quality of the STABYLO scheme is evaluated in this section.

-Three metrics are computed in these experiments : 
+Four metrics are computed in these experiments : 

 the Peak Signal to Noise Ratio (PSNR), 
 the Peak Signal to Noise Ratio (PSNR), 

-the PSNR-HVS-M~\cite{PSECAL07,psnrhvsm11} and the BIQI~\cite{MB10,biqi11}.
+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 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.
+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 &      &      \\
+\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}

 
 
 
 

-biqi = 28.3
-psnr-hvs-m= 78,6
-psnr-hvs= 67.3
+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}
 
 
 \subsection{Steganalysis}


@@ -35,4 +56,22 @@ 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
 \JFC{Raphael, il faut donner des résultats ici}
\ No newline at end of file