用迭代降噪算法实现图信号低比特量化，保真度高。
Low-Bit Quantization of Bandlimited Graph Signals via Iterative Methods
- 基于图拉普拉斯谱特性与图非相干性设计迭代量化方法
- 无需顶点替换的采样方案在真实与合成图上均表现稳健
- 理论保证随机采样性能，适合低带宽图信号处理场景
我们研究图上实值带限信号的量化问题，重点关注低比特表示。提出一种迭代噪声整形量化算法，包含有无顶点替换的采样方法。该方法利用图拉普拉斯的谱特性并借助图非相干性，实现高保真近似。针对随机采样方法给出了理论保证，并通过大量合成与真实图数据集上的数值实验，验证了所提方案的高效性与鲁棒性。
原文摘要 · Abstract (English)
We study the quantization of real-valued bandlimited signals on graphs, focusing on low-bit representations. We propose iterative noise-shaping algorithms for quantization, including sampling approaches with and without vertex replacement. The methods leverage the spectral properties of the graph Laplacian and exploit graph incoherence to achieve high-fidelity approximations. Theoretical guarantees are provided for the random sampling method, and extensive numerical experiments on synthetic and real-world graphs illustrate the efficiency and robustness of the proposed schemes.
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