Fast PNN Using Partial Distortion Search
CAIP '01 Proceedings of the 9th International Conference on Computer Analysis of Images and Patterns
Iterative shrinking method for clustering problems
Pattern Recognition
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A clustering algorithm for the design of efficient 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 can be used as an alternative to the entropy-constrained vector quantizer design (ECVQ) proposed by Chou, Lookabaugh, and Gray (1989). By a natural extension of the ECPNN algorithm the authors develop another algorithm that designs alphabet and entropy-constrained vector quantizers and call it alphabet- and entropy-constrained pairwise nearest neighbor (AECPNN) design. Through simulations on synthetic sources, it is shown 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 (Rao and Pearlman, 1993) algorithms. The advantages over ECVQ are that the ECPNN approach enables much faster codebook design and uses smaller codebooks. A single pass through the ECPNN (or AECPNN) design algorithm, which progresses from larger to successively smaller rates, allows the storage of any desired number of intermediate codebooks. In the context of multirate subband (or transform) coders, this feature is especially desirable. The performance of coding image pyramids using ECPNN and AECPNN codebooks at rates from 1/3 to 1.0 bit/pixel is discussed