A theory of nonsubtractive dither

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How researchers see this paper

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Distributed Training with Heterogeneous Data: Bridging Median- and Mean-Based Algorithms (2019, methodology): “Further, we found our perturbation mechanism share some similarity with "dither" [Wannamaker et al., 2000] which aims at reducing artifacts in quantized signals by adding noise before quantization.”
Distributed Average Consensus With Dithered Quantization (2008, methodology): “Dithering has been widely recognized as a method to render the quantization noise independent of the quantized data, reducing some artifacts created by deterministic quantization and there is a vast literature on the topic, see [22] and references therein.”
Distributed Consensus Algorithms in Sensor Networks: Quantized Data and Random Link Failures (2007, background): “In [28], we considered consensus when the topologies are random (links fail or become alive at random times) and the links are noisy (analog noise) simultaneously.”