标题： 量化随机投影的分布式编码 显示英文标题

标题： Distributed Coding of Quantized Random Projections

摘要： 在本文中，我们提出了一种用于结构化源的分布式源编码的新框架，例如稀疏信号。 我们的框架利用了线性逆问题理论以及使用不相容投影进行信号表示的最新进展。 我们的方法获取并量化信号的不相容线性测量值，这些测量值被表示为单独的比特平面。 每个比特平面使用适当速率的分布式源编码进行编码，并传输。 解码器从最不显著的比特平面开始，并使用信号的预测作为辅助信息，根据源预测和信号迭代地恢复每个比特平面，假设所有较低显著性的先前比特平面已经恢复。 我们提供了指导速率选择的理论结果，仅依赖于源的最小二乘预测误差。 这与现有方法形成对比，现有方法依赖于难以估计的信息论度量来设置速率。 我们通过在遥感多光谱图像上的仿真验证了我们的方法，并将其与具有相似复杂度的现有方法进行了比较。 显示英文摘要

摘要： In this paper we propose a new framework for distributed source coding of structured sources, such as sparse signals. Our framework capitalizes on recent advances in the theory of linear inverse problems and signal representations using incoherent projections. Our approach acquires and quantizes incoherent linear measurements of the signal, which are represented as separate bitplanes. Each bitplane is coded using a distributed source code of the appropriate rate, and transmitted. The decoder, starts from the least significant biplane and, using a prediction of the signal as side information, iteratively recovers each bitplane based on the source prediction and the signal, assuming all the previous bitplanes of lower significance have already been recovered. We provide theoretical results guiding the rate selection, relying only on the least squares prediction error of the source. This is in contrast to existing approaches which rely on difficult-to-estimate information-theoretic metrics to set the rate. We validate our approach using simulations on remote-sensing multispectral images, comparing them with existing approaches of similar complexity.