Rate distortion in image coding from embedded optimization constraints in vector quantization

We compared the performance of two recently developed vector quantization algorithms using different optimization criteria for clustering, namely, the adaptive fuzzy leader clustering, a neuro-fuzzy algorithm, and the deterministic annealing, another unsupervised clustering algorithm based on probabilistic and statistical physics frameworks, with the rate distortion criterion as a performance measure. Such a comparison is useful for evaluating the efficiency of clustering algorithms for the purpose of image vector quantization instead of the conventional misclassification evaluation. This method is extended from the analysis of image coding in a spatial domain to sample vectors in the wavelet domain with predictable distribution. These sample vectors possess a multidimensional generalized Gaussian distribution through the new multi-scale feature extraction method. Our preliminary results show much improvement on the reconstructed image quality over JPEG