向量量化综述

吴泽斌; 于俊清

doi:10.1631/FITEE.1700833

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向量量化综述

常规文章 | Updated：2022-05-19

- 向量量化综述
  Enhanced Publication
- Vector quantization: a review
- 信息与电子工程前沿（英文） 2019年20卷第4期页码：507-524
- Affiliations：
  
  Department of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
  Center of Network and Computation, Huazhong University of Science and Technology, Wuhan 430074, China
- Author bio：
  
  [ "Ze-bin WU, E-mail: zbwu@hust.edu.cn" ]
  Jun-qing YU, E-mail: yjqing@hust.edu.cn
- Funds：
  
  Project supported by the National Natural Science Foundation of China (Nos. 61572211, 61173114, and 61202300)
- DOI：10.1631/FITEE.1700833
  中图分类号： TP391
- 纸质出版日期：2019-04，
  
  收稿日期：2017-12-12，
  
  修回日期：2019-04-11，
- Accepted：
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吴泽斌, 于俊清. 向量量化综述[J]. 信息与电子工程前沿（英文）, 2019,20(4):507-524.

ZE-BIN WU, JUN-QING YU. Vector quantization: a review. [J]. Frontiers of information technology & electronic engineering, 2019, 20(4): 507-524.
吴泽斌, 于俊清. 向量量化综述[J]. 信息与电子工程前沿（英文）, 2019,20(4):507-524. DOI： 10.1631/FITEE.1700833.

ZE-BIN WU, JUN-QING YU. Vector quantization: a review. [J]. Frontiers of information technology & electronic engineering, 2019, 20(4): 507-524. DOI： 10.1631/FITEE.1700833.

摘要

向量量化用于语音与图像编码可有效减小带宽和存储开销。根据码书生成过程，可将传统向量量化方法分为7类：树形向量量化、直和向量量化、迪卡尔积向量量化、格子向量量化、基于分类的向量量化、反馈向量量化以及模糊向量量化。在过去10年中，基于向量量化的近似近邻搜索发展迅速，涌现大量在大规模数据集内存中搜索图像的编码方法。这些方法的一个显著特征是使用多个码书，形成两种新的码书结构：线性组合码书和联合码书，这将成为未来发展趋势。这些方法用于近似近邻搜索的本质是在速度、准确率和空间开销之间权衡，有时其中一个会受损。因此，找到一个在速度、准确率和空间开销中平衡的向量量化方法依然是一个值得研究的问题。

Abstract

Vector quantization (VQ) is a very effective way to save bandwidth and storage for speech coding and image coding. Traditional vector quantization methods can be divided into mainly seven types

tree-structured VQ

direct sum VQ

Cartesian product VQ

lattice VQ

classified VQ

feedback VQ

and fuzzy VQ

according to their codebook generation procedures. Over the past decade

quantization-based approximate nearest neighbor (ANN) search has been developing very fast and many methods have emerged for searching images with binary codes in the memory for large-scale datasets. Their most impressive characteristics are the use of multiple codebooks. This leads to the appearance of two kinds of codebook: the linear combination codebook and the joint codebook. This may be a trend for the future. However

these methods are just finding a balance among speed

accuracy

and memory consumption for ANN search

and sometimes one of these three suffers. So

finding a vector quantization method that can strike a balance between speed and accuracy and consume moderately sized memory

is still a problem requiring study.

关键词

近似近邻搜索图像编码向量量化

Keywords

Approximate nearest neighbor searchImage codingVector quantization

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