基于熵编码和高斯过程回归的多端传感器源编码

Samuel Cheng
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引用次数: 6

摘要

只提供摘要形式。在本文中,我们采用了与编码社区不同的方法。我们没有采用通常的量化加睡眠狼编码的方法,而是在发送端不执行任何睡眠狼编码。我们简单地对传感器读数进行量化,用传统的熵编码压缩量化指标,并将压缩后的指标发送给接收器。在解码器端,我们简单地执行熵解码和高斯过程回归来重建联合源。为了降低所有传感器的和速率,一些传感器被删减,不向解码器传输任何东西。
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Multiterminal Source Coding for Many Sensors with Entropy Coding and Gaussian Process Regression
Summary form only given. In this paper, we take a different approach from the coding community. Instead of taking the usual route of quantization plus Slepian-Wolf coding, we do not perform any Slepian-Wolf coding on the transmitter side. We simply perform quantization on the sensor readings, compress the quantization indexes with conventional entropy coding, and send the compressed indexes to the receiver. On the decoder side, we simply perform entropy decoding and Gaussian process regression to reconstruct the joint source. To reduce the sum rate over all sensors, some sensors are censored and do not transmit anything to the decoder.
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