Image Mapping with Cumulative Distribution Function for Quick Convergence of Counter Propagation Neural Networks in Image Compression
In general the images used for compression are of
different types like dark image, high intensity image etc. When these
images are compressed using Counter Propagation Neural Network,
it takes longer time to converge. The reason for this is that the given
image may contain a number of distinct gray levels with narrow
difference with their neighborhood pixels. If the gray levels of the
pixels in an image and their neighbors are mapped in such a way that
the difference in the gray levels of the neighbor with the pixel is
minimum, then compression ratio as well as the convergence of the
network can be improved. To achieve this, a Cumulative Distribution
Function is estimated for the image and it is used to map the image
pixels. When the mapped image pixels are used the Counter
Propagation Neural Network yield high compression ratio as well as
it converges quickly.
[1] Rafael C. Gonzalez, Richard E.Woods, Digital Image Processing, 2nd
Ed., (PHI, 2005).
[2] Simon Haykin, Neural Networks A Comprehensive Foundation. Second
Edition, (Pearson Education 2001).
[3] James A. Freeman, David M. Skapura, Neural Networks Algorithms,
Applications and Programming Techniques, Pearson Education, 2004,
213 - 262.
[4] Marjan Vracko, Kohonen Artificial Neural Network and Counter
Propagation Neural Network in Molecular Structure-Toxicity Studies,
Current Computer-Aided Drug Design, 2005,1,73-78.
[5] K. M. Ashikur Rahman and Chowdhury Mofizur Rahman, New
Approach for Compressing Color Images using Neural Network:
CIMCA 2003 Proceeding/ISBN 1740880684; M.Mohammadian (Ed.)
12-14 February 2003, Vienna-Austria.
[6] Hamdy S. Soliman Ahmed Abdelali, Toward Lossless Image
Compression, ISCA 8, 2003.
[7] Rafael C. Gonzalez, Richard E. Woods, Steven L.Eddins, Digital Image
Processing Using Matlab, (Pearson).
[1] Rafael C. Gonzalez, Richard E.Woods, Digital Image Processing, 2nd
Ed., (PHI, 2005).
[2] Simon Haykin, Neural Networks A Comprehensive Foundation. Second
Edition, (Pearson Education 2001).
[3] James A. Freeman, David M. Skapura, Neural Networks Algorithms,
Applications and Programming Techniques, Pearson Education, 2004,
213 - 262.
[4] Marjan Vracko, Kohonen Artificial Neural Network and Counter
Propagation Neural Network in Molecular Structure-Toxicity Studies,
Current Computer-Aided Drug Design, 2005,1,73-78.
[5] K. M. Ashikur Rahman and Chowdhury Mofizur Rahman, New
Approach for Compressing Color Images using Neural Network:
CIMCA 2003 Proceeding/ISBN 1740880684; M.Mohammadian (Ed.)
12-14 February 2003, Vienna-Austria.
[6] Hamdy S. Soliman Ahmed Abdelali, Toward Lossless Image
Compression, ISCA 8, 2003.
[7] Rafael C. Gonzalez, Richard E. Woods, Steven L.Eddins, Digital Image
Processing Using Matlab, (Pearson).
@article{"International Journal of Information, Control and Computer Sciences:51326", author = "S. Anna Durai and E. Anna Saro", title = "Image Mapping with Cumulative Distribution Function for Quick Convergence of Counter Propagation Neural Networks in Image Compression", abstract = "In general the images used for compression are of
different types like dark image, high intensity image etc. When these
images are compressed using Counter Propagation Neural Network,
it takes longer time to converge. The reason for this is that the given
image may contain a number of distinct gray levels with narrow
difference with their neighborhood pixels. If the gray levels of the
pixels in an image and their neighbors are mapped in such a way that
the difference in the gray levels of the neighbor with the pixel is
minimum, then compression ratio as well as the convergence of the
network can be improved. To achieve this, a Cumulative Distribution
Function is estimated for the image and it is used to map the image
pixels. When the mapped image pixels are used the Counter
Propagation Neural Network yield high compression ratio as well as
it converges quickly.", keywords = "Correlation, Counter Propagation Neural Networks,Cummulative Distribution Function, Image compression.", volume = "2", number = "4", pages = "1031-6", }