A FAST MEAN-DISTANCE-ORDERED PARTIAL CODEBOOK SEARCH ALGORITHM FOR IMAGE VECTOR QUANTIZATION

A new fast search algorithm for vector quantization using the mean of image vectors is proposed. The codevectors are sorted according to their component means, and the search for the codevector having the minimum Euclidean-distance to a given input vector starts with the one having the minimum mean-distance to it, making use of our observation that the two codevectors are close to each other in most real images. 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. Simulations show that the number of calculations can be reduced to as low as a fourth the number achievable by an algorithm known as the partial distance method.