An Improved image Compression approach with SOFM Network using Cumulative Distribution Function

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 that the given image may contain a number of distinct gray levels with narrow difference with their neighbourhood pixels. If the gray levels of the pixels in an image and their neighbours are mapped in such a way that the difference in the gray levels of the neighbours 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 Map network yields high compression ratio as well as it converges quickly.

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