Fast codebook search algorithms based on tree-structured vector quantization

Chang, Chin‐Chen; Li, Yu-Chiang; Yeh, Jun-Bin

doi:10.1016/j.patrec.2005.12.017

Cited by 30 publications

(9 citation statements)

References 16 publications

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“…Each node of the tree examines only one component of the training vector. The study in [24] presents a full search equivalent TSVQ (FSE-TSVQ) approach, which is an improvement to TSVQ. It can obtain the closest codevector for each input vector.…”

Section: Related Workmentioning

confidence: 99%

Fast VQ codebook generation via Pattern Reduction

Lee

Tsai

Chiang

et al. 2008

2008 IEEE International Conference on Systems, Man and Cybernetics

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In this paper, we present a simple but fast codebook generation algorithm, called PREGLA (Pattern Reduction Enhanced GLA). The proposed algorithm is fundamentally different from the previous approaches in that the latter focuses on reducing the size of the codebook whereas the former focuses on using pattern reduction to reduce the computation time. The proposed algorithm is motivated by the observation that input vectors that are "static" during the training process can be considered as part of the final solutions and thus can be compressed and removed to eliminate the redundant computations at the later iterations of the training process. To evaluate the performance of the proposed algorithm, we compare the proposed algorithm with "efficient" VQ algorithms published recently such as Nearest Partition Set Search, Fast Vector Quantization Algorithm, and standard GLA. Our experimental results indicate that the proposed algorithm can reduce the computation time from 27.86% up to about 80.45% compared to that of standard GLA and other fast GLA-based algorithms alone.

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Section: Related Workmentioning

confidence: 99%

Fast VQ codebook generation via Pattern Reduction

Lee

Tsai

Chiang

et al. 2008

2008 IEEE International Conference on Systems, Man and Cybernetics

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“…Conventionally, VQ conducts a full search to ensure that a codeword is best matched with an arbitrary input vector, but the full search requires an enormous computational load. Thus, a continuous effort has been made to simplify the search complexity of an encoding process in a great volume of published studies [14][15][16][17][18][19][20][21][22][23][24][25]. These approaches are further classified into three types in terms of the way the complexity is simplified, including the tree-structured VQ (TSVQ) techniques [14][15][16], the TIE-based approaches [18][19][20] and the equal-average equal-variance equal-norm nearest neighbor search (EEENNS) based algorithms [21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

A Search Complexity Improvement of Vector Quantization to Immittance Spectral Frequency Coefficients in AMR-WB Speech Codec

2016

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An adaptive multi-rate wideband (AMR-WB) code is a speech codec developed on the basis of an algebraic code-excited linear-prediction (ACELP) coding technique, and has a double advantage of low bit rates and high speech quality. This coding technique is widely used in modern mobile communication systems for a high speech quality in handheld devices. However, a major disadvantage is that a vector quantization (VQ) of immittance spectral frequency (ISF) coefficients occupies a significant computational load in the AMR-WB encoder. Hence, this paper presents a triangular inequality elimination (TIE) algorithm combined with a dynamic mechanism and an intersection mechanism, abbreviated as the DI-TIE algorithm, to remarkably improve the complexity of ISF coefficient quantization in the AMR-WB speech codec. Both mechanisms are designed in a way that recursively enhances the performance of the TIE algorithm. At the end of this work, this proposal is experimentally validated as a superior search algorithm relative to a conventional TIE, a multiple TIE (MTIE), and an equal-average equal-variance equal-norm nearest neighbor search (EEENNS) approach. With a full search algorithm as a benchmark for search load comparison, this work provides a search load reduction above 77%, a figure far beyond 36% in the TIE, 49% in the MTIE, and 68% in the EEENNS approach.

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“…Conventionally, VQ conducts a full search to ensure a codeword best matched with an arbitrary input vector, but a full search requires an enormous computational load. Thus, as in a great volume of published studies [7][8][9][10][11][12][13][14][15][16], a continuous effort has been made to simplify the search complexity of an encoding process. These approaches are further classified into two types in terms of the way the complexity is simplified.…”

Section: Introductionmentioning

confidence: 99%