Picture coding using pseudo-random noise

By Lawrence G. Roberts. 1962

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In Signal Processing

Citations in the corpus

Gradient Descent with Compressed Iterates (2019, neutral): “Among compression operators used in quantized distributed stochastic gradient methods, compression operators satisfying Assumption 2 are ubiquitous and include natural compression [9], dithering [8, 27], natural dithering [9], sparsification [29], ternary quantization [35], and others.”
Natural Compression for Distributed Deep Learning (2019, methodology): “Standard (random) dithering, D sta , [13, 39] is obtained as a special case of general dithering (which is also novel) for a linear partition of the unit interval, ls−1 = 1/s, ls−2 = 2/s, .”
Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication (2019, background): “A class of very common quantization operators is based on random dithering (Goodall, 1951; Roberts, 1962) that is in addition also unbiased, Eξ Q(x) = x, ∀x ∈ R, see (Alistarh et al.”
End-to-end optimization of nonlinear transform codes for perceptual quality (2016, methodology): “Dithering was used in some early image coders [17], but generally does not improve rate–distortion performance.”
Stochastic Distributed Learning with Gradient Quantization and Variance Reduction (2019, background): “A class of very common quantization operators is based on random dithering (Goodall, 1951; Roberts, 1962) and can be described as the random operators for random variable ξ ∼ u . a . r .”
Uncertainty Principle for Communication Compression in Distributed and Federated Learning and the Search for an Optimal Compressor (2020, unknown): “…sparsiﬁcation (Koneˇcn´y & Richt´arik, 2018; Wangni et al., 2018), top-k sparsiﬁcation (Alistarh et al., 2018), standard random dithering (Goodall, 1951; Roberts, 1962; Alistarh et al., 2017), natural dithering (Horv´ath et al., 2019a), ternary quantization (Wen et al., 2017), and scaled sign…”
A theory of nonsubtractive dither (2000, methodology): “Subtractively dithered (SD) quantizing systems, in which the dither is subsequently subtracted from the output signal after quantization, have been discussed and used for over 30 years in speech and video processing applications [ 1 ], [2], and a satisfactory theory of their operation exists in prin”