Quantization for Signal Compression, Classification, and Mixture Modeling

信号压缩、分类和混合建模的量化

• 批准号: 0309701
• 负责人: Robert Gray
• 金额: $ 63.79万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-07-01 至 2007-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0309701&HistoricalAwards=false
• 关键词: Quantization; Signal; Compression; Classification; Mixture

Abstract0309701Robert M. GrayStanford University The research is about the theory and design of systems for communicating and interpreting information bearing signals such as images, video, and speech. The main focus is on methods for reducing the complexity of signals while retaining or extracting essential information. The algorithms typically quantize or compress the data efficiently into a simpler signal that can be transmitted or stored efficiently and then used in place of the original. They can facilitate identification of the data as belonging to some class or type. Examples of such signal processing include analog-to-digital conversion, speech and image coding, speech recognition, and segmenting images into distinct regions of interest. The research involves the fundamental theory of such systems and draws on ideas from information theory, statistical signal processing, and statistics. It also involves applications, especially algorithms for classifying and segmenting images and for content-addressable browsing through image databases.The basic tools come from modeling, density estimation, compression, coding, classification, and segmentation. They enable both theoretical characterizations of optimal performance and associated algorithms by which codes are optimized for specific applications, for example, systems for compression and segmentation of images for communications, analysis, and retrieval. Tools are drawn from four fundamental ideas of information theory and signal processing: vector quantization, mixture models, relative entropy (Kullback-Leibler information) measures of distortion or distance between probability distributions, and universal coding. Of particular interest is the theory of high rate vector quantization and its applications to statistical image classification and segmentation. Specific applications of interest include automaticsegmentation of multimodal images and of categorizing images taken internally in gas pipelines in order to quantify pipeline integrity.

摘要0309701罗伯特·M·格雷斯坦福大学 该研究涉及用于通信和解释信息承载信号（例如图像、视频和语音）的系统的理论和设计。主要重点是在保留或提取基本信息的同时降低信号复杂性的方法。 这些算法通常将数据有效地量化或压缩为更简单的信号，该信号可以有效地传输或存储，然后代替原始信号使用。它们可以帮助将数据识别为属于某个类或类型。此类信号处理的示例包括模数转换、语音和图像编码、语音识别以及将图像分割成不同的感兴趣区域。 该研究涉及此类系统的基础理论，并借鉴了信息论、统计信号处理和统计学的思想。它还涉及应用程序，特别是用于对图像进行分类和分割以及通过图像数据库进行内容可寻址浏览的算法。基本工具来自建模、密度估计、压缩、编码、分类和分割。 它们既可以实现最佳性能的理论表征，也可以实现相关算法，通过这些算法可以针对特定应用优化代码，例如用于通信、分析和检索的图像压缩和分割系统。工具源自信息论和信号处理的四个基本思想：矢量量化、混合模型、失真或概率分布之间距离的相对熵（Kullback-Leibler 信息）度量以及通用编码。 特别令人感兴趣的是高速矢量量化理论及其在统计图像分类和分割中的应用。 感兴趣的具体应用包括多模态图像的自动分割以及对天然气管道内部拍摄的图像进行分类，以量化管道的完整性。