Use of Kohonen´s self-organizing network as a pre-quantizer

Kohonen´s network has the ability to achieve near optimal quantization of the input space. The Kohonen´s training algorithm adapts very quickly to the input space and requires much less computation. Many experiments are carried out to compare the performance of the LBG algorithm and the Kohonen´s algorithm. The variables used are the dimensionality of the input space and the level of organization. The results are found to confirm the faster adaptation of the Kohonen´s algorithm although the final distortion levels are slightly higher. A combination of the two approaches is suggested to achieve lower distortion values with less training