Spectra of quantized signals

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Price of Transparency in Strategic Machine Learning (2016, background): “This assumption, also known as the high resolution (high rate) quantization theory [10], holds for asymptotically high number of clusters and dithered quantizers.”
A Survey of Quantization Methods for Efficient Neural Network Inference (2021, neutral): “Other important historical research on quantization in signal processing in that time period includes [181], which introduced the Pulse Code Modulation (PCM) concept (a pulsing method proposed to approximate/represent/encode sampled analog signals), as well as the classical result of high resolution”
Introduction to Digital Speech Processing (2007, background): “the correlations are equal to the negative of the error variance [12].”
A theory of nonsubtractive dither (2000, methodology): “Although is clearly a deterministic function of the input, the classical model of quantization (CMQ) [ 34 ], [35] holds that the quantization error can be modeled as an additive random process that is independent of the system input and iid (i.e., that distinct samples of the error are statistically”
Introduction to Data Compression (2005, background): “Bennett [119], and it has been widely used to analyze quantizers.”
Scalar Quantization for Relative Error (2011, background): “A more general class of quantizers can be constructed using a compander [13] as shown in Figure 1 .”