Low-Bit Quantization of Bandlimited Graph Signals via Iterative Methods
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Abstract (arXiv)
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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References · 9
- 2015An Introduction to Matrix Concentration Inequalitiescited by 397
- 2020Adaptive Graph Encoder for Attributed Graph Embeddingcited by 89
- 2013Vertex-Frequency Analysis on Graphscited by 25
- 2020A Greedy Algorithm for Quantizing Neural Networkscited by 22
- 2022A simple approach for quantizing neural networkscited by 8
- 2021Approximation of functions with one-bit neural networkscited by 5
- 2020Sigma Delta quantization for imagescited by 2
- 2022Enhanced Digital Halftoning via Weighted Sigma-Delta Modulationcited by 2
- 2024The Mathematics of Dots and Pixels: On the Theoretical Foundations of Image Halftoningcited by 1
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- 2025Graph is a Natural Regularization: Revisiting Vector Quantization for Graph Representation Learning
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