• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Mathematics of Data/Image Pattern Recognition, Compression and Encryption with Applications IX >Perspectives on data compression for estimations from sensors
【24h】

Perspectives on data compression for estimations from sensors

机译：从传感器进行数据压缩的观点

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

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：数据压缩方法主要集中在针对给定数量的比特获得多媒体数据的期望感知质量。但是，在过去的几十年中，人们一直对将数据传送到数据用于计算估计值的远程位置的压缩感兴趣。本文追溯了研究文献中用于估计压缩的观点。我们讨论如何将这些观点全部转换为以下形式：源发出信号-可能取决于某些未知参数，第i个传感器接收信号并将其压缩以传输到使用该信号的中央处理中心进行估算。可以将先前的观点归类为优化压缩，以达到（ⅰ）源信号估计或（ⅱ）源参数的目的。早期结果集中在将编码器限制为根据某些优化标准设计的标量量化器。后来的结果集中在更通用的压缩结构上，尽管其中大多数集中在建立信息理论的结果和界限上。作者的最新结果使用运算速率失真方法来开发任务驱动的压缩算法，该算法允许在给定速率下在多个估计任务之间进行权衡。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号