Kossentini F, Smith M T
Dept. of Electr. Eng., British Columbia Univ., Vancouver, BC, Canada.
IEEE Trans Image Process. 1998;7(7):1045-50. doi: 10.1109/83.701164.
A clustering algorithm based on the pairwise nearest-neighbor (PNN) algorithm developed by Equitz (1989), is introduced for the design of entropy-constrained residual vector quantizers. The algorithm designs residual vector quantization codebooks by merging the pair of stage clusters that minimizes the increase in overall distortion subject to a given decrease in entropy. Image coding experiments show that the clustering design algorithm typically results in more than a 200:1 reduction in design time relative to the standard iterative entropy-constrained residual vector quantization algorithm while introducing only small additional distortion. Multipath searching over the sequence of merges is also investigated and shown experimentally to slightly improve rate-distortion performance. The proposed algorithm can be used alone or can he followed by the iterative algorithm to improve the reproduction quality at the same bit rate.
介绍了一种基于Equitz（1989）开发的成对最近邻（PNN）算法的聚类算法，用于设计熵约束残差矢量量化器。该算法通过合并阶段聚类对来设计残差矢量量化码本，在给定熵减少的情况下，使整体失真的增加最小化。图像编码实验表明，与标准迭代熵约束残差矢量量化算法相比，聚类设计算法通常可将设计时间减少200倍以上，同时仅引入少量额外失真。还研究了在合并序列上的多路径搜索，并通过实验表明其可略微提高率失真性能。所提出的算法可以单独使用，也可以在迭代算法之后使用，以在相同比特率下提高再现质量。
