在LDGM码的因子图上使用具有抽取的置信传播的二进制quantization
Binary quantization using Belief Propagation with decimation over factor graphs of LDGM codes
摘要：
提出了一种基于低密度生成矩阵（LDGM）码因子图抽取的置信传播算法的二进制量化新算法。我们称之为Bias Propagation（BiP）的这种算法可以被认为是Wainwright等人[8]提出的用于二进制量化的Survey Propagation算法的特例。它实现了相同的接近最佳的率失真性能，具有更简单的框架和10-100倍的速度。因此，我们挑战的普遍信念，稀疏线性码的基础上的二进制量化不能解决简单的信念传播算法。最后，我们给出了适当的不规则LDGM代码的例子，与BiP算法的工作，并显示其性能。
英文摘要：
We propose a new algorithm for binary quantization based on the Belief Propagation algorithm with decimation over factor graphs of Low Density Generator Matrix (LDGM) codes. This algorithm, which we call Bias Propagation (BiP), can be considered as a special case of the Survey Propagation algorithm proposed for binary quantization by Wainwright et al. [8]. It achieves the same near-optimal rate-distortion performance with a substantially simpler framework and 10-100 times faster implementation. We thus challenge the widespread belief that binary quantization based on sparse linear codes cannot be solved by simple Belief Propagation algorithms. Finally, we give examples of suitably irregular LDGM codes that work with the BiP algorithm and show their performance.
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