Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  concerns task-based image quality assessment for the task of discriminating beten two classes of images.  address the problem of estimating two widely-used detection performance measures, SNR and AUC, from a finite number of images, assuming that the class discrimination is performed with a channelized Hotelling observer. In particular,  investigate the advantage that can be gained when either 1) the means of the signal-absent and signal-present classes are both known, or 2) when the difference of class means is known. For these two scenarios,  propose uniformly minimum variance unbiased estimators of SNR 2 , derive the corresponding sampling distributions and provide variance expressions. In addition,  demonstrate how the bias and variance for the related AUC estimators may be calculated numerically by using the sampling distributions for the SNR 2  estimators.  find that for both SNR 2  and AUC, the new estimators have significantly lor bias and mean-square error than the traditional estimator, which assumes that the class means, and their difference, are unknown.
['Adam Wunderlich', 'Frédéric Noo']
I am writing a research paper on a idea Estimation of Channelized Hotelling Observer Performance With Known Class Means or Known Difference of Class Means
[{"from": "human", "value": "I am writing a research paper on a idea Estimation of Channelized Hotelling Observer Performance With Known Class Means or Known Difference of Class Means"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  concerns task-based image quality assessment for the task of discriminating beten two classes of images.  address the problem of estimating two widely-used detection performance measures, SNR and AUC, from a finite number of images, assuming that the class discrimination is performed with a channelized Hotelling observer. In particular,  investigate the advantage that can be gained when either 1) the means of the signal-absent and signal-present classes are both known, or 2) when the difference of class means is known. For these two scenarios,  propose uniformly minimum variance unbiased estimators of SNR 2 , derive the corresponding sampling distributions and provide variance expressions. In addition,  demonstrate how the bias and variance for the related AUC estimators may be calculated numerically by using the sampling distributions for the SNR 2  estimators.  find that for both SNR 2  and AUC, the new estimators have significantly lor bias and mean-square error than the traditional estimator, which assumes that the class means, and their difference, are unknown."}]