Image compression via optimal vector quantization: a comparison between SOM, LBG and k-means algorithms
Javier Corral, María José Abásolo Guerrero, Pedro J. Zufiria · 1994
An application of optimal vector quantization on image compression is studied. A neural network structure for obtaining the optimal codebook for the vector quantizer (VQ) is employed. This structure is based on Kohonen's self-organizing map (SOM), whose learning algorithm provides an optimal codebook for a training sequence. It is demonstrated that the SOM complies, in general, with the Max-Lloyd's conditions for optimal VQ. In this line, it is shown that the obtained codebook minimizes the averaged distortion over the training sequence, provided that the certain regularity conditions are satisfied. Finally, SOM-VQ convergence properties and squared-mean-error results are compared with LBG as well as k-means algorithms.>