我听说您对近邻搜索的复杂性有深入的研究,尤其是在乘积量化(PQ)、倒排多索引(IVQ)和层次可导航小世界图(HNSW)等方法上。我来这里是为了学习这些方法的细节,首先从基于乘积量化的近邻搜索过程开始。它具体是如何展开的? 阿基米德: 啊,莱奥尼达斯,今天的确是一个学习的好日子。让我们深入探讨一下基于乘积量化(PQ)...
VQ, 即Vector Quantization,矢量量化,在多个场景下使用,如图像压缩,声音压缩,语音识别等。 什么是VQ? Vector quantizer(矢量量化器)将矢量空间Rk中的 k 维矢量映射到矢量的有限集合Y={yi:i=1,2,...,N}。 每个向量yi称为code vector(代码向量)或codeword(码字)。 所有码字的集合称为codebook(码本)。 与每...
矢量量化(VQ,Vector Quantization)是一种极其重要的信号压缩方法。VQ在语音信号处理中占十分重要的地位。广泛应用于语音编码、语音识别和语音合成等领域。 一、概述 VectorQuantization (VQ)是一种基于块编码规则的有损数据压缩方法。事实上,在 JPEG 和 MPEG-4 等多媒体压缩格式里都有 VQ 这一步。它的基本思想是:...
在接下去说其他的聚类算法之前,让我们先插进来说一说一个有点跑题的东西:Vector Quantization。这项技术广泛地用在信号处理以及数据压缩等领域。事实上,在 JPEG 和 MPEG-4 等多媒体压缩格式里都有 VQ 这一步。 Vector Quantization 这个名字听起来有些玄乎,其实它本身并没有这么高深。大家都知道,模拟信号是连续的...
Tree-structured vector quantizationVector quantizationVoronoï partitionIntroduction Rationale Optimum codebook generation Optimum quantizer performance Using the quantizer Gain-shape vector quantizationdoi:10.1002/9781118616611.ch2Nicolas MoreauJohn Wiley & Sons, Inc....
矢量量化（VQ —Vector Quantization）是70年代后期发展起来的一种数据压缩技术基本思想：将若干个标量数据组构成一个矢量，然后在矢量空间给以整体量化，从而压缩了数据而不损失多少信息。矢量量化编码也是在图像、语音信号编码技术中研究得较多的新型量化编码方法，它的出现并不仅仅是作为量化器设计而提出的，...
Vector quantization refers to a popular technique used in approximate nearest neighbor (ANN) search, where the data space is divided into subspaces and a residual structure is created for each subspace. This structure, known as Residual Vector Product Quantization (RVPQ), is learned using an effec...
Vector quantization is simply a multidimensional extension of the zero-memory (one-dimensional) quantization scheme. From: Encyclopedia of Physical Science and Technology (Third Edition), 2003 About this pageSet alert Also in subject areas:
A new type of objective function is proposed for solving vector quantization problems by means of neural networks of Hopfield type. The extension of the domain of the function gives an energy function, by which a neuro-dynamical system is introduced. It is proved that any minimal point of the...
TL;DRExisting vector-quantization (VQ) based autoregressive image generation simply modelsall local regioninformation of imageswithout distinguishing their different perceptual importancein the first stage, which brings redundancy in the learned codebook that not only limits the next stage’s autoregressive ...