What is the significance of mean squared error in signal fidelity measures?
Best insight from top research papers
The Mean Squared Error (MSE) is traditionally used to measure signal fidelity in various applications, including image quality assessment and quantum teleportation. However, research suggests that MSE alone may not effectively capture perceptual fidelity. In quantum error correction schemes, fidelity is commonly used but may not always be the best figure of merit, as it does not provide information on the exact location of errors. In the context of quantum teleportation, along with average fidelity, considering the second moment of fidelity (fidelity deviation) provides a more comprehensive assessment of protocol efficiency, especially in noisy scenarios. Therefore, while MSE is a widely used metric, incorporating additional measures like fidelity deviation can enhance the assessment of signal fidelity in various applications.
Answers from top 5 papers
More filters
| Papers (5) | Insight |
|---|---|
| Mean squared error, or fidelity deviation, complements average fidelity in assessing continuous-variable teleportation efficiency by capturing fidelity variations, offering a more comprehensive evaluation of protocol performance. | |
| In continuous-variable teleportation, mean squared error, or fidelity deviation, alongside average fidelity, is crucial for assessing protocol efficiency and characterizing Gaussian and non-Gaussian states accurately. | |
23 Sep 2005 | The provided paper does not discuss the significance of mean squared error in signal fidelity measures. |
Open access•Posted Content | The provided paper does not discuss the significance of mean squared error in signal fidelity measures. |
01 Dec 2013 | The paper states that Mean Squared Error (MSE) is not an effective index to describe the perceptual fidelity of images. It is inconsistent with human perception of image quality and ignores structural relationships and noise correlations. |
Related Questions
What is Mean Squared Error (MSE)?5 answersMean Squared Error (MSE) is a crucial performance measure in estimation methods for systems like Multiple-Input Multiple-Output (MIMO) systems. It quantifies the average squared difference between the estimated values and the actual values. MSE plays a significant role in evaluating the accuracy and convergence properties of estimation techniques. Various methods, such as continuous-time processing and one-bit quantized systems, utilize MSE for optimization and analysis. The MSE can be influenced by parameters like regularization parameters, which impact convergence rates. Additionally, MSE expressions can be derived for different types of estimators, aiding in performance predictions and comparisons in various scenarios.
What is mean square error analogue for distribution?5 answersThe mean square error (MSE) is a well-established tool for assessing closeness to a target value when bias and sampling error are taken into account. In the context of distribution, the mean integrated squared error (MISE) is a measure used to evaluate the performance of kernel estimators of a cumulative distribution function. The MISE provides an exact, closed form expression for the performance of Gaussian-based kernel estimators, which have been found to perform well in small and large samples but may be suboptimal in moderate samples. Additionally, the synthetic MSE control chart is a combination of the standard MSE control chart and the conforming run length (CRL) control chart, used for monitoring changes in the mean and standard deviation of a normally distributed process.
What is the usage of mean percentage error?5 answersThe mean absolute percentage error (MAPE) is widely used as a measure of prediction accuracy in businesses and organizations. However, it has been shown that MAPE is biased and favors methods that under-forecast. This bias can lead to incorrect model selection. An alternative measure based on the forecast to actual ratio has been proposed, which overcomes the bias issue for strictly positive data. This alternative measure estimates the geometric mean and possesses a form of unbiasedness appropriate for relative accuracy. It is suggested that this measure is preferable to MAPE for practical use..
How do i do a error analysis on the mean calculation?5 answersError analysis on the mean calculation can be performed by evaluating the deviation of the calculated values from the actual values. This can be done by comparing the results obtained from numerical calculations with known analytical solutions or experimental data. The validity of the results can be assessed using criteria such as the Pau Ta Criterion. Additionally, the asymptotic errors made in the numerical integration can be investigated to understand the accuracy of the calculations. The mean squared error can be derived to determine the exact asymptotics of the error. By studying the errors of nonlinear force-free (NLFF) fields, the mean values of absolute relative standard deviations (RSD) can be obtained to estimate the deviation of extrapolated fields from the real force-free field.
Which can we use root mean squared error or mean absolite error?1 answersBoth the root mean squared error (RMSE) and the mean absolute error (MAE) can be used, depending on the characteristics of the error distribution and the specific requirements of the analysis. RMSE is preferred for platykurtic distributions, while MAE is preferred for leptokurtic distributions. For mesokurtic distributions, either RMSE or MAE can be used. However, using the two estimators alone can lead to erroneous conclusions when comparing errors with different distribution types. It is important to consider the error distribution, sample size, and standard errors of the estimators when comparing errors. Additionally, the estimated RMSE/MSE ratio can help identify the type of error distribution. A combination of metrics, including RMSE and MAE, is often required to assess model performance.
How to prove that the expected value of mean square errors of the multiple linear regression model pdf?5 answersThe expected value of mean square errors in a multiple linear regression model can be proven using the abstracts provided. The method of proof is based on a functional limit theorem for the least absolute deviation (LAD) objective function. Additionally, non-standard approaches such as the polynomial maximization method (PMM) can be used to find estimates of linear regression parameters for asymmetrically distributed errors. Asymptotic expansions for the standardized and studentized least squares estimates in multiple linear regression models can also be obtained without assuming normal errors. Furthermore, the use of multiple criteria estimation in linear regression models can be motivated and proposed as an alternative to the least squares criterion. Overall, these approaches provide different methods for estimating and analyzing the accuracy of parameters in a multiple linear regression model, allowing for the examination of mean square errors.