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Stack filters and the mean absolute error criterion
IEEE Transactions on Acoustics, Speech, and Signal Processing, 1988A method to determine the stack filter which minimizes the mean absolute error between its output and a desired signal, given noisy observations of this desired signal, is presented. Specifically, an optimal window-width-b stack filter can be determined with a linear program with O(b2/sup b/) variables.
E J Coyle, J -H Lin
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Rank order operators and the mean absolute error criterion
IEEE Transactions on Acoustics, Speech, and Signal Processing, 1988The analysis of the mean absolute error for rank order filters is discussed. These filters are a subset of a larger class of nonlinear filters, called stack filters, which obey two basic properties: a) a weak superposition property, the so-called threshold decomposition and b) an ordering property called the stacking property.
E J Coyle
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A Weighted Mean Absolute Error Metric for Image Quality Assessment
2020 IEEE International Conference on Visual Communications and Image Processing (VCIP), 2020Pixel-wise image quality assessment (IQA) algorithms, such as mean square error (MSE), mean absolute error (MAE) and peak signal-to-noise ratio (PSNR) correlate well with perceptual quality when dealing with images sharing the same distortion type but not well when processing images in different distortion types, which is inconsistent with human visual
Sumei Li
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Assessing the statistical characteristics of the mean absolute error or forecasting
International Journal of Forecasting, 1991Abstract This paper assesses some general statistical characteristics of the mean absolute error of forecasting (MAEF). It shows that the MAEF is the sample estimate of the expected value of the absolute error of forecasting, and derives its mean and variance.
Wen Lea Pearn
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Adaptive stack filtering under the mean absolute error criterion
IEEE Transactions on Acoustics, Speech, and Signal Processing, 1990An adaptive filter algorithm is developed for the class of stack filters, which is a class of nonlinear filters obeying a weak superposition property. The adaptation algorithm can be interpreted as a learning algorithm for a group of decision-making units, the decisions of which are subject to a set of constraints called the stacking constraints. Under
J -H Lin, E J Coyle
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On the mean squared error, the mean absolute error and the like
Communications in Statistics - Theory and Methods, 1999The problem of finding the minimizer of the rth -mean error , is revisited, via a unified approach. The approach is discussed for arbitrary r and is illustrated for r = 1 (mean absolute error)r = 2 (mean squared error), and r = 4. This approach is also discussed in the context of maximum likelihood estimation in a class of symmetric distributions which
Shaul K. Bar-Lev +2 more
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Minimum mean absolute error nonlinear filtering
ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing, 2003A class of sliding window operators called generalized stack filters is developed. This class of filters, which includes all rank order filters, stack filters, and digital morphological filters, is the set of all filters possessing the threshold decomposition architecture and a consistency property, called the stacking property.
Jean-Hsang Lin, Edward J. Coyle
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Quantization Based on the Mean-Absolute-Error Criterion
IEEE Transactions on Communications, 1978Performance criteria for the design of optimum quantizers are considered. A distance criterion for quantizer input and output probability distribution functions is formulated, and its relationship to the usual distortion criteria is established.
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Exact Mean Absolute Error of Baseline Predictor, MARP0
Information and Software Technology, 2016Shepperd and MacDonell "Evaluating prediction systems in software project estimation". Information and Software Technology 54 (8), 820-827, 2012, proposed an improved measure of the effectiveness of predictors based on comparing them with random guessing.
William B. Langdon +3 more
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Morphological filter mean-absolute-error theorem
SPIE Proceedings, 1992The general characterization of optimal morphological filters is based on the Matheron representation for morphological filters. As conceived in its most general form, optimal- morphological-filter design involves a search over potential bases of structuring elements that can be used to form the Matheron erosion expansion.
Robert P. Loce, Edward R. Dougherty
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