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

S. Yang, S. Mitra

Research output: Contribution to conferencePaperpeer-review

1 Scopus citations


In this paper, we have compared the performance of two recently developed vector quantization algorithms using different optimization criteria for clustering, namely, the adaptive fuzzy leader clustering (AFLC) [3], a neuro-fuzzy algorithm, and deterministic annealing (DA) [4], 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 analysis of image coding in the spatial domain to sample vectors in the wavelet domain with predictable distribution. These sample vectors possess multi-dimensional generalized Gaussian distribution through a new multi-scale feature extraction method. Our preliminary results show much improvement on reconstructed image quality over JPEG.

Original languageEnglish
Number of pages6
StatePublished - 2001
EventInternational Joint Conference on Neural Networks (IJCNN'01) - Washington, DC, United States
Duration: Jul 15 2001Jul 19 2001


ConferenceInternational Joint Conference on Neural Networks (IJCNN'01)
Country/TerritoryUnited States
CityWashington, DC


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