Results 291 to 300 of about 2,392,377 (346)

A Weighted Mean Absolute Error Metric for Image Quality Assessment

2020 IEEE International Conference on Visual Communications and Image Processing (VCIP), 2020
Pixel-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
Sihan Hao, Sumei Li
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Exact Mean Absolute Error of Baseline Predictor, MARP0

Information and Software Technology, 2016
Shepperd 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.
Langdon, WB, Dolado, J, Sarro, F, Harman, M   +3 more
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Quantization Based on the Mean-Absolute-Error Criterion

IEEE Transactions on Communications, 1978
Performance 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.
S. Kassam
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Morphological correlation and mean absolute error criteria

International Conference on Acoustics, Speech, and Signal Processing, 2003
The mean absolute error criterion for signal detection and matching is linked with a morphological signal correlation (a sum of minima). Several properties of this nonlinear correlation are investigated, its performance for signal detection is compared with that of the classical (sum of products) linear correlation, and its statistical form is ...
P. Maragos
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Stack filters and the mean absolute error criterion

IEEE Transactions on Acoustics, Speech, and Signal Processing, 1988
A 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.
Coyle, Edward J., Lin, Jean-Hsang
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Rank order operators and the mean absolute error criterion

IEEE Transactions on Acoustics, Speech, and Signal Processing, 1988
The 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. Coyle
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Adaptive stack filtering under the mean absolute error criterion

IEEE Transactions on Acoustics, Speech, and Signal Processing, 1990
An 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, T. M. Sellke, E. J. Coyle
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Optimal parallel stack filtering under the mean absolute error criterion

IEEE Transactions on Image Processing, 1994
The authors extend the configuration of stack filtering to develop a new class of stack-type filters called parallel stack filters (PSFs). As a basis for the parallel stack filtering, the block threshold decomposition (BTD) is introduced, and its properties are investigated.
Zeng, Bing, Neuvo, Yrjo
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The mean-absolute-error criterion for quantization

ICASSP '77. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005
A significant property of the mean-absolute-error (MAE) criterion for optimum quantization is derived. A criterion based on output probability distributions is first formulated, and the two equations for optimum quantizer parameters based on this criterion are obtained.
S. Kassam
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Mean-Absolute-Error Representation and Optimization of Computational-Morphological Filters

Graphical Models and Image Processing, 1995
Abstract Computational mathematical morphology provides a framework for analysis and representation of range-preserving, finite-range operators in the context of mathematical morphology. As such, it provides a framework for statistically optimal design in the framework of a Matheron-type representation; that is, each increasing, translation-invariant
R.P. Loce, E.R. Dougherty
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