CIF: Small: Sampling Rate Distortion

CIF：小：采样率失真

• 批准号: 1319799
• 负责人: Prakash Narayan
• 金额: $ 49.97万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1319799&HistoricalAwards=false
• 关键词: CIF; Small; Sampling; Rate; Distortion

This research takes an information theoretic approach to understanding principles that govern a coordinated rate-efficient sampling of multiple signals and centralized compression of the sampled subset. The goal is to reconstruct the entirety of the signals within acceptable distortion levels. The research has three main components: an integrated analysis of sampling and rate distortion behavior, and their tradeoffs; sampling rate distortion theory for Markov random fields; and sampling rate distortion performance for signals with memory. Expected outcomes are a characterization of fundamental performance limits of optimum sampling rate and lossy compression rate and their interplay, together with the best choice of sampling mechanisms and attendant processing for reconstruction.The investigators' technical approach involves the development of a principle of "sampling rate distortion" which lies at the intersection of specialities in information theory and signal processing, and has the larger objective of elucidating material connections between sampling and rate distortion performance. The performance of specific sampling and rate distortion processing schemes will be investigated. Specific groups of open problems chosen for investigation address a general class of multisignal models for sampling and lossy data compression. These are motivated by potential applications including dynamic thermal management for on-chip temperature control during runtime; network function computation; and image restoration, surface reconstruction and visual integration in computer vision.

本研究采用信息论方法来理解多个信号的协调速率有效采样和采样子集的集中压缩的原理。目标是在可接受的失真水平内重建整个信号。该研究有三个主要组成部分：采样和率失真行为的综合分析，以及它们的权衡;马尔可夫随机场的采样率失真理论;以及具有记忆的信号的采样率失真性能。预期结果是最佳采样率和有损压缩率及其相互作用的基本性能极限的表征，以及采样机制的最佳选择和重建的伴随处理。研究人员的技术方法涉及“采样率失真”原理的发展，该原理位于信息论和信号处理专业的交叉点，并且具有阐明采样和率失真性能之间的实质联系的更大目标。具体的采样和率失真处理方案的性能将进行调查。特定组的开放式问题的调查地址一般类的多信号模型的采样和有损数据压缩。这些都是由潜在的应用，包括动态热管理芯片上的温度控制在运行时;网络功能计算;和图像恢复，表面重建和计算机视觉中的视觉集成。