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 Self-Organizing Feature Maps, 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 Self-Organizing Feature Maps yield high compression ratio as well as it converges quickly.

Keywords: Self-Organizing Feature Maps,Cumulative Distribution Function, Learning Vector Quantization, Correlation, Convergence, Pixel value.

BibTex:

@ARTICLE{P1150630002,

AUTHOR = {S.Anna Durai B.E.M.E., M.E and E. Anna Saro MCA, M.Phil},

TITLE = {An Improved Image Compression approach with Self-Organizing Feature Maps using Cumulative Distribution Function},

JOURNAL ={ICGST International Journal on Graphics, Vision and Image Processing},

}

(Full Paper 1,245KB)