A comparison of the LBG algorithm and Kohonen neural network paradigm for image vector quantization

The creation of an acceptable codebook, as defined by three methods of measuring performance (peak signal-to-noise ratio, image quality, and entropy), is discussed and how the Linde-Buzo-Gray (LBG) and Kohonen neural network (KNN) methods differ detailed. The results show that the codebooks generated by these two methods both enable low bits-per-pixel coding with low distortion. When using fewer training vectors, and when given a suboptimal initial codebook, the KNN method outperformed the LBG. For a theoretical lower bound, mean square error comparisons to an optimal N-level k-dimensional quantizer lower bound were made using a Gaussian source. As k increased, the KNN performance came quite close to the optimal quantizer