quantization

简明释义

英[ˌkwɒntɪˈzeɪʃən]美[ˌkwɑːntɪˈzeɪʃən]

n. [量子] 量子化；分层；数字化

英英释义

单词用法

同义词

反义词

例句

1.Vector Quantization is an effective data compression technology.

矢量量化是一种有效的数据压缩技术。

2.No one, until this time, had suggested that a system would be subjected to quantization except for light.

在他之前，还没有人提出过,除光系统外的量子化系统。

3.VQ (Vector Quantization) technique is widely used in text-dependent and text-independent speaker recognition systems.

矢量量化(VQ)技术在话者识别系统中得到了广泛的应用。

4.Financial quantization study supporting system is a automatically electronic transaction system of whether ones buy or sell.

金融量化研究支持系统是一个自动撮合买进、卖出委托的电子化交易系统。

5.In quantization step quantization gene is an important parameter in video stream control. The enactment of this parameter is determined by buffer occupancy.

量化阶段量化因子是一个控制码率的重要参数，这个参数的设定主要是结合缓冲区的占用容量。

6.Then , the coefficients are quantized by quantization matrix.

然后对变换系数应用量化矩阵进行量化。

7.The quantization error can significantly affect the accuracy of the digital representation of an analog signal.

由于量化误差，可能会显著影响模拟信号的数字表示精度。

8.The quantization of audio signals can lead to artifacts that degrade sound quality.

音频信号的量化可能导致降质的伪影，从而降低音质。

9.In machine learning, quantization can be used to reduce the model size and improve inference speed on mobile devices.

在机器学习中，量化可以用于减少模型大小并提高移动设备上的推理速度。

10.In digital signal processing, quantization refers to the process of mapping a continuous range of values into a finite range of discrete values.

在数字信号处理中，量化指的是将连续值范围映射到有限的离散值范围的过程。

11.During image processing, quantization reduces the number of colors in an image to decrease file size.

在图像处理中，量化减少图像中的颜色数量以减小文件大小。

作文

In the realms of physics and computer science, the term quantization refers to the process of constraining an item from a large set to a smaller set, typically in the context of converting continuous values into discrete values. This concept is fundamental in various fields, including quantum mechanics, digital signal processing, and even machine learning. Understanding quantization is essential for grasping how modern technology operates, particularly in the way we handle data and information.To begin with, let’s explore quantization in the context of quantum mechanics. In this field, quantization is the process by which certain physical properties, such as energy levels, are restricted to discrete values rather than a continuous range. For instance, electrons in an atom can only occupy specific energy levels. When an electron transitions between these levels, it emits or absorbs a photon whose energy corresponds to the difference between the two levels. This discrete nature of energy levels is what gives rise to the distinct spectral lines observed in atomic spectra. Thus, quantization plays a crucial role in explaining the behavior of particles at the quantum level.Moving on to digital signal processing, quantization takes on a different yet equally important meaning. Here, quantization involves converting analog signals, which are continuous in nature, into digital signals that consist of discrete values. This process is vital for the storage and transmission of audio, video, and other types of data in a digital format. For example, when recording sound, an analog waveform is sampled at regular intervals, and each sample is assigned a value based on its amplitude. The range of possible amplitudes is then divided into a limited number of levels, effectively 'rounding' the continuous values to the nearest available level. This step is referred to as quantization, and it is crucial because it allows us to represent complex sounds in a format that computers can handle.In the realm of machine learning, quantization is increasingly becoming a hot topic, especially as models grow larger and more complex. In this context, quantization refers to the technique of reducing the precision of the numbers used to represent model parameters. For instance, instead of using 32-bit floating-point numbers, a model might be quantized to use 8-bit integers. This reduction in precision can significantly decrease the model size and speed up computations without drastically affecting performance. As a result, quantization enables the deployment of machine learning models on resource-constrained devices like smartphones and IoT devices, making advanced AI accessible to a broader audience.In conclusion, quantization is a multifaceted concept that permeates various scientific and technological fields. Whether it is in understanding the discrete nature of energy levels in quantum mechanics, converting analog signals to digital formats in signal processing, or optimizing machine learning models for efficiency, quantization is a key principle that underpins much of modern technology. As we continue to advance in these fields, a firm grasp of quantization will be indispensable for both researchers and practitioners alike, ensuring that we can harness the power of discrete representations in an increasingly digital world.

在物理学和计算机科学的领域中，术语量化指的是将一个大集合中的项目约束到一个较小集合的过程，通常是在将连续值转换为离散值的上下文中。这个概念在多个领域中都是基础，包括量子力学、数字信号处理，甚至机器学习。理解量化对于掌握现代技术的运作方式至关重要，特别是在我们处理数据和信息的方式上。首先，让我们探讨一下量子力学中的量化。在这个领域中，量化是某些物理属性（例如能级）被限制为离散值而非连续范围的过程。例如，原子中的电子只能占据特定的能级。当电子在这些能级之间跃迁时，它会发射或吸收一个光子，其能量与两个能级之间的差值相对应。这种能级的离散性质导致了原子光谱中观察到的独特谱线。因此，量化在解释量子水平上粒子的行为中起着至关重要的作用。接下来，在数字信号处理的领域中，量化又具有不同但同样重要的含义。在这里，量化涉及将模拟信号（本质上是连续的）转换为由离散值组成的数字信号。这个过程对于以数字格式存储和传输音频、视频及其他类型的数据至关重要。例如，在录制声音时，模拟波形在规律的时间间隔进行采样，每个样本根据其幅度分配一个值。然后，将可能的幅度范围划分为有限数量的水平，有效地将连续值“舍入”到最接近的可用水平。这个步骤被称为量化，它是关键，因为它使我们能够以计算机可以处理的格式表示复杂的声音。在机器学习领域，量化正越来越成为一个热门话题，尤其是随着模型变得越来越大和复杂。在这个背景下，量化指的是减少用于表示模型参数的数字精度的技术。例如，模型可能从使用32位浮点数被量化为使用8位整数。这种精度的降低可以显著减少模型大小并加快计算速度，而不会严重影响性能。因此，量化使得在资源受限的设备（如智能手机和物联网设备）上部署机器学习模型成为可能，使先进的人工智能能够惠及更广泛的受众。总之，量化是一个多方面的概念，渗透到各种科学和技术领域中。无论是在理解量子力学中能级的离散性质，还是在信号处理中的模拟信号转数字格式，亦或是在提高机器学习模型效率的优化过程中，量化都是支撑现代技术的关键原则。随着我们在这些领域的不断进步，牢牢掌握量化将对研究人员和从业者都至关重要，确保我们在日益数字化的世界中能够利用离散表示的力量。