Fast algorithms for vector quantization picture coding

Abstract

Two methods for reducing the computation involved in vector quantization picture coding are presented. First, a data structure (k-d trees, developed by Bentley) is demonstrated to be appropriate for implementing exact nearest neighbor searching in time logarithmic in codebook size. Second, the Pairwise Nearest Neighbor (PNN) algorithm is presented as an alternative to the generalized Lloyd (Linde-Buzo-Gray) algorithm. The PNN algorithm derives a vector quantization codebook in a diminishingly small fraction of the time previously required, without sacrificing performance. Simulations on a variety of images coded at 1/2 bit per pixel indicate that PNN codebooks can be developed in roughly 5% of the time required by the LBG algorithm. The PNN algorithm can be used with squared error and weighted squared error distortion measures. These results are generalizable to any vector quantization application with the appropriate distortion measure.