{A clustering algorithm for the design of e cient vector quantizers to be followed by entropy coding is proposed. The algorithm, called entropy-constrained pairwise nearest neighbor (ECPNN), designs codebooks by merging the pair of Voronoi regions which gives the least increase in distortion for a given decrease in entropy. The algorithm is an entropy-constrained version of the pairwise nearest neighbor (PNN) clustering algorithm of Equitz and can be used as an alternative to the entropyconstrained vector quantizer design (ECVQ) proposed by Chou, Lookabaugh and Gray. By a natural extension of the ECPNN algorithm we develop another algorithm that designs alphabet-and entropy-constrained vector quantizers and call it alphabet-and entropy-constrained pairwise nearest neighbor (AECPNN) design. The AECPNN algorithm can be used as alternative to the alphabet-and entropy-constrained vector quantizer design (AECVQ) proposed by Rao and Pearlman, that is directly based on the ECVQ design algorithm. Through simulations on synthetic sources, we show that ECPNN and ECVQ have indistinguishable mean-square-error versus rate performance and that the ECPNN and AECPNN algorithms obtain as close performance by the same measure as the ECVQ and AECVQ algorithms. The advantages over

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