Image Compression using GSOM Algorithm
Shabbir Ahmad · Zenodo (CERN European Organization for Nuclear Research) · 2014
This paper presents a neural network based technique that may be applied to image compression. Conventional techniques such as Huffman coding and the Shannon Fano method, LZ Method, Run Length Method, LZ-77 are more recent methods for the compression of data. A traditional approach to reduce the large amount of data would be to discard some data redundancy and introduce some noise after reconstruction. We present a neural network based Growing self-organizing map technique that may be a reliable and efficient way to achieve vector quantization. Typical application of such algorithm is image compression. Moreover, Kohonen networks realize a mapping between an input and an output space that preserves topology. This feature can be used to build new compression schemes which allow obtaining better compression rate than with classical method as JPEG without reducing the image quality .the experiment result show that proposed algorithm improve the compression ratio in BMP, JPG and TIFF File.