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HDR Imaging From Quantization Noise
2020 IEEE International Conference on Image Processing (ICIP), 2020Quantization is an integral part of image acquisition but also a major performance bottleneck due to the trade-off between dynamic range and resolution. As we discuss in this paper, in contrast, quantization noise can be acquired reliably even beyond the dynamic range by re-purposing recent hardware development.
Ayush Bhandari, Felix Krahmer
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IEEE Transactions on Information Theory, 1990
Several results describing the behavior of quantization noise in a unified and simplified manner are discussed. Exact formulas for quantizer noise spectra are developed. They are applied to a variety of systems and inputs, including scalar quantization (PCM), dithered PCM, sigma-delta modulation, dithered sigma-delta modulation, two-stage sigma-delta ...
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Several results describing the behavior of quantization noise in a unified and simplified manner are discussed. Exact formulas for quantizer noise spectra are developed. They are applied to a variety of systems and inputs, including scalar quantization (PCM), dithered PCM, sigma-delta modulation, dithered sigma-delta modulation, two-stage sigma-delta ...
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Quantization Noise Mitigation via Parallel ADCs
IEEE Signal Processing Letters, 2014In systems with parallel analogue-to-digital converters (ADC), diversity combining can improve the signal to noise ratio (SNR) of the digitised signal. We consider a novel architecture, where the parallel ADCs operate on differently attenuated signals, in conjunction with maximal ratio combining and gain weighted combining.
Andre Pollok +3 more
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Iterative Adaptation to Quantization Noise
2021Quantization allows accelerating neural networks significantly, especially for mobile processors. Existing quantization methods require either training neural network from scratch or gives significant accuracy drop for the quantized model. Low bits quantization (e.g., 4- or 6-bit) task is a much more resource consumptive problem in comparison with 8 ...
Dmitry Chudakov +3 more
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Noise Parameter Estimation from Quantized Data
Proceedings of the 2006 IEEE International Workshop on Advanced Methods for Uncertainty Estimation in Measurement (AMUEM 2006), 2006In this paper, the parametric estimation of the variance of white Gaussian noise is considered when available data are obtained from a quantized noisy stimulus. The Crameacuter-Rao lower bound is derived, and the statistical efficiency of a maximum-likelihood parametric estimator is discussed, along with the estimation algorithm proposed in IEEE ...
MOSCHITTA, Antonio, CARBONE, Paolo
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Noise Fuzzy Learning Vector Quantization
Key Engineering Materials, 2010Fuzzy learning vector quantization (FLVQ) benefits from using the membership values coming from fuzzy c-means (FCM) as learning rates and it overcomes several problems of learning vector quantization (LVQ). However, FLVQ is sensitive to noises because it is a FCM-based algorithm (FCM is sensitive to noises).
Xiao Hong Wu, Bin Wu, Jie Wen Zhao
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2011
In this chapter an abstract model of a noise-shaping quantizer is derived. A noise-shaping quantizer is a more general description of a sigma-delta modulator, consisting of one or more cost functions and a selection function. The cost function is an indication of the quality of a signal or an encoding solution, and the selection function determines the
Janssen, Erwin, van Roermund, Arthur
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In this chapter an abstract model of a noise-shaping quantizer is derived. A noise-shaping quantizer is a more general description of a sigma-delta modulator, consisting of one or more cost functions and a selection function. The cost function is an indication of the quality of a signal or an encoding solution, and the selection function determines the
Janssen, Erwin, van Roermund, Arthur
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Quantization noise in electronic halftoning
Journal of the Optical Society of America A, 1993Several classes of electronic halftone techniques are described that can be applied to binarize graytone images. The difference between the quantized and the corresponding analog image constitutes the quantization noise. The characteristics of the quantization noise can be influenced by the parameters that determine the halftone process.
Manfred Broja, Olof Bryngdahl
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Statistical Properties of the Quantization Noise
IFAC Proceedings Volumes, 1987Abstract In digital control systems, the quantization process can be regarded as the addition of a perturbation to a continuous signal. The characteristics of the so-called quantization noise depend on signal statistics and on quantizing conditions (i.e. number of quantizing levels, clipping ...). The points discussed in the paper are : uniformity of
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Noise analysis in real‐time portal imaging. I. Quantization noise
Medical Physics, 1994The limit at which quantization noise becomes dominant in video‐based real‐time portal imaging has been studied. Quantization noise due to truncation in integer frame averaging is shown to be dominant over the input analog‐to‐digital converter (A/D) quantization noise, unless image addition is used in video‐based real‐time portal imaging systems ...
R, Rajapakshe, S, Shalev
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