A new fast search algorithm for vector quantization using the Feature functions of image vectors is proposed. The codevectors are sorted according to their Feature values, and the search for the codevector having the minimum Euclidean-distance to a given input vector starts with the one having the minimum Feature-distance to it. The search is then made to terminate as soon as a simple yet novel test reports that any remaining vector in the codebook should have a larger Euclidean distance.
The definition of Feature functions is proposed and some examples are presented. The simulation results show that the efficiency of this algorithm is comparable with the TSVQ, which is a suboptimal method.
