Sample-size adaptive self-organization map for color images quantization

The paper presents a sample-size adaptive SOM (SA-SOM) algorithm for color quantization of images to adapt to the variations of network parameters and training sample size. The sweep size of neighborhood function is modulated by the size of the training data. In addition, the minimax distortion principle which is modulated by training sample size is used to search winning neuron. Based on the SA-SOM, we use the sampling ratio of training data, rather than the conventional weight change between adjacent sweeps, as a stop criterion, to significantly speed up the learning process. The experimental results show that the SA-SOM achieves much better PSNR quality, and smaller PSNR variation under various combinations of network parameters.