1 November 1991 Vector quantization of image pyramids with the ECPNN algorithm
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A recent algorithm for single rate vector quantization [1] is used for coding image pyramids. The algorithm, called entropy-constrained pairwise-nearest-neighbor (ECPNN), designs codebooks by merging the pair of Voronoi regions which gives the most decrease in entropy for a given increase in distortion. In terms of performance in the mean-squared-error sense the algorithm produces codebooks with the same performance as the ECVQ design algorithm [1,2]. The main advantage over ECVQ is that ECPNN algorithm enables much faster codebook design. A single pass through the ECPNN design algorithm, which progresses from larger to successively smaller rates, allows the storage of any desired number of optimal intermediate-rate codebooks. In the context of pyramid coding, this feature is especially desirable, since the ECPNN design algorithm must be run for each sub-band and storage of codebooks of different rates are required for each subband. Good results at 0.5 bpp, judged both visually and using peak-to-peak SNR criterion, have been obtained by coding image pyramids using ECPNN codebooks.
© (1991) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Diego Pinto de Garrido, Diego Pinto de Garrido, William A. Pearlman, William A. Pearlman, Weiler A. Finamore, Weiler A. Finamore, } "Vector quantization of image pyramids with the ECPNN algorithm", Proc. SPIE 1605, Visual Communications and Image Processing '91: Visual Communication, (1 November 1991); doi: 10.1117/12.50307; https://doi.org/10.1117/12.50307


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