Image Compression with Back-Propagation Neural Network using Cumulative Distribution Function
Image Compression using Artificial Neural Networks
is a topic where research is being carried out in various directions
towards achieving a generalized and economical network.
Feedforward Networks using Back propagation Algorithm adopting
the method of steepest descent for error minimization is popular and
widely adopted and is directly applied to image compression.
Various research works are directed towards achieving quick
convergence of the network without loss of quality of the restored
image. In general the images used for compression are of different
types like dark image, high intensity image etc. When these images
are compressed using Back-propagation Network, it takes longer
time to converge. The reason for this is, 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 neighbors 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 Back-propagation Neural
Network yields high compression ratio as well as it converges
quickly.
[1] M.Egmont-Petersen, D.de.Ridder, Handels, "Image Processing with
Neural Networks - a review", Pattern Recognition 35(2002) 2279-
2301, www.elsevier.com/locate/patcog
[2] Bogdan M.Wilamowski, Serdar Iplikci, Okyay Kaynak, M. Onder Efe
"An Algorithm for Fast Convergence in Training Neural Networks".
[3] Fethi Belkhouche, Ibrahim Gokcen, U.Qidwai, "Chaotic gray-level
image transformation, Journal of Electronic Imaging -- October -
December 2005 -- Volume 14, Issue 4, 043001 (Received 18 February
2004; accepted 9 May 2005; published online 8 November 2005.
[4] Hahn-Ming Lee, Chih-Ming Cheb, Tzong-Ching Huang, "Learning
improvement of back propagation algorithm by error saturation
prevention method", Neurocomputing, November 2001.
[5] Mohammed A.Otair, Walid A. Salameh, "Speeding up Back-propagation
Neural Networks" Proceedings of the 2005 Informing Science and IT
Education Joint Conference.
[6] M.A.Otair, W.A.Salameh (Jordan), "An Improved Back-Propagation
Neural Networks using a Modified Non-linear function", The IASTED
Conference on Artificial Intelligence and Applictions, Innsbruck,
Austria, February 2006.
[7] Simon Haykin, "Neural Networks - A Comprehensive foundation", 2nd
Ed., Pearson Education, 2004.
[8] B.Verma, B.Blumenstin and S. Kulkarni, Griggith University, Australia,
"A new Compression technique using an artificial neural network".
[9] Rafael C. Gonazalez, Richard E.Woods, "Digital Image Processing", 2nd
Ed., PHI, 2005.
[1] M.Egmont-Petersen, D.de.Ridder, Handels, "Image Processing with
Neural Networks - a review", Pattern Recognition 35(2002) 2279-
2301, www.elsevier.com/locate/patcog
[2] Bogdan M.Wilamowski, Serdar Iplikci, Okyay Kaynak, M. Onder Efe
"An Algorithm for Fast Convergence in Training Neural Networks".
[3] Fethi Belkhouche, Ibrahim Gokcen, U.Qidwai, "Chaotic gray-level
image transformation, Journal of Electronic Imaging -- October -
December 2005 -- Volume 14, Issue 4, 043001 (Received 18 February
2004; accepted 9 May 2005; published online 8 November 2005.
[4] Hahn-Ming Lee, Chih-Ming Cheb, Tzong-Ching Huang, "Learning
improvement of back propagation algorithm by error saturation
prevention method", Neurocomputing, November 2001.
[5] Mohammed A.Otair, Walid A. Salameh, "Speeding up Back-propagation
Neural Networks" Proceedings of the 2005 Informing Science and IT
Education Joint Conference.
[6] M.A.Otair, W.A.Salameh (Jordan), "An Improved Back-Propagation
Neural Networks using a Modified Non-linear function", The IASTED
Conference on Artificial Intelligence and Applictions, Innsbruck,
Austria, February 2006.
[7] Simon Haykin, "Neural Networks - A Comprehensive foundation", 2nd
Ed., Pearson Education, 2004.
[8] B.Verma, B.Blumenstin and S. Kulkarni, Griggith University, Australia,
"A new Compression technique using an artificial neural network".
[9] Rafael C. Gonazalez, Richard E.Woods, "Digital Image Processing", 2nd
Ed., PHI, 2005.
@article{"International Journal of Information, Control and Computer Sciences:56134", author = "S. Anna Durai and E. Anna Saro", title = "Image Compression with Back-Propagation Neural Network using Cumulative Distribution Function", abstract = "Image Compression using Artificial Neural Networks
is a topic where research is being carried out in various directions
towards achieving a generalized and economical network.
Feedforward Networks using Back propagation Algorithm adopting
the method of steepest descent for error minimization is popular and
widely adopted and is directly applied to image compression.
Various research works are directed towards achieving quick
convergence of the network without loss of quality of the restored
image. In general the images used for compression are of different
types like dark image, high intensity image etc. When these images
are compressed using Back-propagation Network, it takes longer
time to converge. The reason for this is, 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 neighbors 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 Back-propagation Neural
Network yields high compression ratio as well as it converges
quickly.", keywords = "Back-propagation Neural Network, Cumulative
Distribution Function, Correlation, Convergence.", volume = "2", number = "5", pages = "1503-5", }