期刊名称：International Journal of Advanced Research In Computer Science and Software Engineering
印刷版ISSN：2277-6451
电子版ISSN：2277-128X
出版年度：2013
卷号：3
期号：2
出版社：S.S. Mishra
摘要：Image compression algorithm is needed that will reduce the amount of Image to be transmitted, stored and analyzed, but without losing the information content.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 self-o rganizing Kohonen 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.
