Image Compression using self-Organizing Maps
Christophe Amerijckx, Jean-Didier Legat, Michel Verleysen · Systems Analysis Modelling Simulation · 2003
The self-organizing Kohonen map is 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 to obtain better compression rate than with classical method as JPEG without reducing the image quality. Compared to JPEG, our lossy compression scheme shows better performances (in terms of PSNR) for compression rates higher than 30 [C. Amerijckx, M. Verleysen, P. Thissen and J.-D. Legat (1998). Image compression by self-organized Kohonen map. IEEE Transactions on Neural Networks, 9(3), 503-507.]. For lossless compression, this rate is about 2.7 for standard images.