titre a chier

[canny.git] / experiments.tex

diff --git a/experiments.tex b/experiments.tex
index 60cce0baf515c9c50f2f9568152dffcf568db2ab..710164cd1a24aaccf279b5cd4802edb683a21362 100644 (file)
--- a/experiments.tex
+++ b/experiments.tex
@@ -16,7 +16,7 @@ Canny algorithm with high threshold.
 The message length is thus defined to be the half of this set cardinality.
 In this strategy, two methods are thus applied to extract bits that 
 are modified. The first one is a direct application of the STC algorithm.
-This method is further refered as \emph{adaptive+STC}.
+This method is further referred as \emph{adaptive+STC}.
 The second one randomly choose the subset of pixels to modify by 
 applying the BBS PRNG again. This method is denoted \emph{adaptive+sample}.
 Notice that the rate between 
@@ -57,8 +57,11 @@ The other last ones have been designed to tackle this problem.
 \begin{center}
 \begin{tabular}{|c|c|c||c|c|}
 \hline
- &   \multicolumn{2}{|c||}{Adaptive} & fixed & HUGO \\
-Embedding rate &   + STC &  + sample & 10\% & 10\%\\ 
+Schemes & \multicolumn{3}{|c|}{STABYLO} & HUGO\\
+\hline
+Embedding &   \multicolumn{2}{|c||}{Adaptive} & Fixed & Fixed \\
+\hline
+Rate &   + STC &  + sample & 10\% & 10\%\\ 
 \hline
 PSNR &  66.55 & 63.48  & 61.86  & 64.65   \\ 
 \hline
@@ -70,21 +73,21 @@ wPSNR & 86.43& 80.59 & 77.47& 83.03\\
 \hline
 \end{tabular}
 \end{center}
-\caption{Quality measures of our steganography approach\label{table:quality}} 
+\caption{Quality Measures of Steganography Approaches\label{table:quality}} 
 \end{table}
 
 Let us give an interpretation of these experiments.
 First of all, the adaptive strategy produces images with lower distortion 
 than the one of images resulting from the 10\% fixed strategy.
 Numerical results are indeed always greater for the former strategy than 
-for the latter, except for the BIQI metrics where differences are not relevent.
+for the latter, except for the BIQI metrics where differences are not relevant.
 These results are not surprising since the adaptive strategy aims at 
 embedding messages whose length is decided according to a higher threshold
 into the edge detection.  
 Let us focus on the quality of HUGO images: with a given fixed 
 embedding rate (10\%) 
 HUGO always produces images whose quality is higher than the STABYLO's one.
-However, our approach nevertheless provides beter results with the strategy 
+However, our approach nevertheless provides better results with the strategy 
 adaptive+STC in a lightweight manner, as motivated in the introduction.      
 
 
@@ -130,13 +133,13 @@ can be a favourably executed thanks to an Ensemble Classifiers.
 \hline
 Schemes & \multicolumn{3}{|c|}{STABYLO} & HUGO\\
 \hline
-Embedding rate &  \multicolumn{2}{|c|}{Adaptive} & 10 \% &  10 \%\\
- &   + STC &  + sample &  & \\ 
-
+Embedding &   \multicolumn{2}{|c|}{Adaptive} & Fixed & Fixed \\
+\hline
+Rate &   + STC &  + sample & 10\% & 10\%\\ 
 \hline
-AUMP & 0.39  & & 0.22     &  0.50     \\
+AUMP & 0.39  & 0.33  & 0.22     &  0.50     \\
 \hline
-Ensemble Classifier & 0.47 &  & 0.35     & 0.48     \\
+Ensemble Classifier & 0.47 & 0.44 & 0.35     & 0.48     \\
 
 \hline
 \end{tabular}