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Perspectives on data compression for estimations from sensors

机译：关于传感器估计数据压缩的透视图

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Data compression methods have mostly focused on achieving a desired perception quality for multi-media data for a given number of bits. However, there has been interest over the last several decades on compression for communicating data to a remote location where the data is used to compute estimates. This paper traces the perspectives in the research literature for compression-for-estimation. We discuss how these perspectives can all be cast in the following form: the source emits a signal - possibly dependent on some unknown parameter(s), the ith sensor receives the signal and compresses it for transmission to a central processing center where it is used to make the estimate(s). The previous perspectives can be grouped as optimizing compression for the purpose of either (ⅰ) estimation of the source signal or (ⅱ) the source parameter. Early results focused on restricting the encoder to being a scalar quantizer that is designed according to some optimization criteria. Later results focused on more general compression structures, although, most of those focus on establishing information theoretic results and bounds. Recent results by the authors use operational rate-distortion methods to develop task-driven compression algorithms that allow trade-offs between the multiple estimation tasks for a given rate.
机译：数据压缩方法已经主要集中于实现用于多媒体数据的期望的感知质量的比特的给定数。然而，出现了在过去几十年的利息压缩用于将数据传送到数据用于计算估计远程位置。本文追溯研究文献的角度对压缩的估计。我们将讨论如何这些观点都可以在以下形式浇铸：源发射的信号 - 可能依赖于一些未知参数（多个），第i个传感器接收的信号，并将其压缩，以便传输到它被使用的中央处理中心使估计（S）。以前的观点可被分组为源信号或（ⅱ）的源参数的任一（ⅰ）估计的目的优化压缩。早期的结果集中在限制编码器之处在于，根据一些优化标准设计了一个标量量化。后来的结果集中在更一般的压缩结构，虽然其中大多数集中在建立信息理论成果和界限的。由作者最近的研究结果使用可操作率失真方法来开发任务驱动的压缩算法，考虑给定速率多任务估计之间的权衡。

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