X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/canny.git/blobdiff_plain/1bfb9cc8f38edd065a52de421a0dc41567c4262c..b4843601742819057d2d60095cc2adb85f5b267b:/experiments.tex?ds=inline

diff --git a/experiments.tex b/experiments.tex
index 2419d30..e3d243b 100644
--- a/experiments.tex
+++ b/experiments.tex
@@ -1,7 +1,10 @@
-For whole experiments, a set of 500 images is randomly extracted 
-from the database taken from the BOSS contest~\cite{Boss10}. 
+For whole experiments, the whole set of 10000 images 
+of the BOSS contest~\cite{Boss10} database is taken. 
 In this set, each cover is a $512\times 512$
-grayscale digital image.
+grayscale digital image in a RAW format. 
+We restrict experiments to 
+this set of cover images since this paper is more focussed on 
+the methodology than benchmarking.    
 
 
 \subsection{Adaptive Embedding Rate} 
@@ -44,8 +47,8 @@ Otherwise, pixels are again randomly chosen with BBS.
 The visual quality of the STABYLO scheme is evaluated in this section.
 For the sake of completeness, four metrics are computed in these experiments: 
 the Peak Signal to Noise Ratio (PSNR), 
-the PSNR-HVS-M family~\cite{PSECAL07,psnrhvsm11}, 
-the BIQI~\cite{MB10,biqi11}, and 
+the PSNR-HVS-M family~\cite{psnrhvsm11}, 
+the BIQI~\cite{MB10}, and 
 the weighted PSNR (wPSNR)~\cite{DBLP:conf/ih/PereiraVMMP01}.
 The first one is widely used but does not take into
 account the Human Visual System (HVS).