Performance Re-Evaluation on Codewords Distribution-Based Optimal Combination of Equal-Average Equal-Variance Equal-Norm Nearest Neighbor Fast Search Algorithm for Vector Quantization Encoding

Yang Wang

doi:10.1109/TIP.2017.2763820

Performance Re-Evaluation on Codewords Distribution-Based Optimal Combination of Equal-Average Equal-Variance Equal-Norm Nearest Neighbor Fast Search Algorithm for Vector Quantization Encoding

2018-02-01

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AI

DOI：10.1109/TIP.2017.2763820

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In the re-evaluated paper, Xie et al. proposed a new fast search algorithm for vector quantization encoding, which optimized the priority checking order of variance and norm inequality in order to speed up the encoding procedure. CPU time of different encoding algorithms is given to support their algorithm. However, first, some of the experimental data in the re-evaluated paper are unreasonable and unrepeatable. And second, as an improved algorithm of equal-average equal-variance equal-norm nearest neighbor fast search algorithm, the re-evaluated algorithm in fact cannot achieve a better performance than the existing improved equal-average equal-variance nearest neighbor fast search algorithm. In this paper, these two problems are analyzed, re-evaluated, and discussed in detail.

Keyword:

Vector quantization

codewords distribution

fast search algorithm

priority checking order

Performance Re-Evaluation on Codewords Distribution-Based Optimal Combination of Equal-Average Equal-Variance Equal-Norm Nearest Neighbor Fast Search Algorithm for Vector Quantization Encoding

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