CIF: Small: Sampling Rate Distortion

本研究采用信息理论方法来理解控制多个信号的协调高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：采样和速率失真行为的综合分析及其权衡；马尔可夫随机场的采样率畸变理论；以及有记忆信号的采样率失真性能。预期结果是对最佳采样率和有损压缩率的基本性能限制及其相互作用的描述，以及采样机制的最佳选择和随之而来的重建处理。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速率失真性能之间的物质联系的更大目标。具体的采样和率失真处理方案的性能将进行研究。选择用于研究的开放问题的特定组解决了用于采样和有损数据压缩的一般多信号模型。这些都是由潜在的应用驱动的，包括运行期间芯片上温度控制的动态热管理；网络函数计算；以及计算机视觉中的图像恢复、表面重建和视觉集成。

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.