tarata

[canny.git] / experiments.tex

diff --git a/experiments.tex b/experiments.tex
index 45f0b87967d5052182e0ffe4d39255c77edaf16f..cd2ed1a23e9decfd8794c7fb511859a5c321a823 100644 (file)
--- a/experiments.tex
+++ b/experiments.tex
@@ -9,7 +9,7 @@ Practically, a set of edge pixels is computed according to the
 Canny algorithm with high threshold.
 The message length is thus defined to be the half of this set cardinality.
 The rate between  available bits  and bit message length is then more than two.This constraint is indeed induced by the fact that the efficiency 
 Canny algorithm with high threshold.
 The message length is thus defined to be the half of this set cardinality.
 The rate between  available bits  and bit message length is then more than two.This constraint is indeed induced by the fact that the efficiency 

-of the stc algorithm is unsatisfactory under that threshold.
+of the STC algorithm is unsatisfactory under that threshold.

 
 
 In the latter, the embedding rate is defined as a percentage between the 
 
 
 In the latter, the embedding rate is defined as a percentage between the 


@@ -18,7 +18,7 @@ This is the classical approach adopted in steganography.
 Practically, the Canny algorithm generates a 
 a set of edge pixels with threshold that is decreasing until its cardinality
 is sufficient. If the set cardinality is more than twice larger than the 
 Practically, the Canny algorithm generates a 
 a set of edge pixels with threshold that is decreasing until its cardinality
 is sufficient. If the set cardinality is more than twice larger than the 

-bit message length an stc is again applied.
+bit message length an STC step is again applied.

 Otherwise, pixels are randomly chosen from the set of pixels to build the 
 subset with a given size. The BBS PRNG is again applied there.
  
 Otherwise, pixels are randomly chosen from the set of pixels to build the 
 subset with a given size. The BBS PRNG is again applied there.
  


@@ -27,7 +27,7 @@ subset with a given size. The BBS PRNG is again applied there.
 
 \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.

-Four metrics are computed in these experiments : 
+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 Peak Signal to Noise Ratio (PSNR), 
 the PSNR-HVS-M family~\cite{PSECAL07,psnrhvsm11} , 
 the BIQI~\cite{MB10,biqi11} and 


@@ -61,9 +61,9 @@ schemes with respect to the image quality.
 Fist off all, wPSNR and PSNR of the Edge Adaptive
 scheme detailed in~\cite{Luo:2010:EAI:1824719.1824720} are lower than ours.
 Next both the approaches~\cite{DBLP:journals/eswa/ChenCL10,Chang20101286}
 Fist off all, wPSNR and PSNR of the Edge Adaptive
 scheme detailed in~\cite{Luo:2010:EAI:1824719.1824720} are lower than ours.
 Next both the approaches~\cite{DBLP:journals/eswa/ChenCL10,Chang20101286}

-focus on increasing the payload while the PSNR is acceptable, bu do not 
+focus on increasing the payload while the PSNR is acceptable, but do not 

 give quality metrics for fixed embedding rate from a large base of images. 
 give quality metrics for fixed embedding rate from a large base of images. 

-Our approach outperforms the former thanks to the introduction of the stc 
+Our approach outperforms the former thanks to the introduction of the STC 

 algorithm.
 
 
 algorithm.
 
 


@@ -97,7 +97,7 @@ Embedding rate &  Adaptive & 10 \% &  10 \%\\
 \hline
 AUMP & 0.39  & 0.22     &  0.50     \\
 \hline
 \hline
 AUMP & 0.39  & 0.22     &  0.50     \\
 \hline

-Ensemble Classifier &   &      &      \\
+Ensemble Classifier & 0.47   & 0.35     & 0.48     \\

 
 \hline
 \end{tabular}
 
 \hline
 \end{tabular}