首页> 外文期刊>Journal of the Optical Society of America, A. Optics, image science, and vision >Objective assessment of image quality. III. ROC metrics, ideal observers, and likelihood-generating functions
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Objective assessment of image quality. III. ROC metrics, ideal observers, and likelihood-generating functions

机译:客观评估图像质量。三, ROC指标,理想观察者和可能性生成函数

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We continue the theme of previous papers [J. Opt. Soc. Am. A 7, 1266 (1990); 12, 834 (1995)] on objective (task-based) assessment of image quality. We concentrate on signal-detection tasks and figures of merit related to the ROC (receiver operating characteristic) curve. Many different expressions for the area under an ROC curve (AUC) are derived for an arbitrary discriminant function, with different assumptions on what information about the discriminant function is available. In particular, it is shown that AUC can be expressed by a principal-value integral that involves the characteristic functions of the discriminant. Then the discussion is specialized to the ideal observer, defined as one who uses the likelihood ratio (or some monotonic transformation of it, such as its logarithm) as the discriminant function. The properties of the ideal observer are examined from first principles. Several strong constraints on the moments of the likelihood ratio or the log likelihood are derived, and it is shown that the probability density functions for these test statistics are intimately related. In particular, some surprising results are presented for the case in which the log likelihood is normally distributed under one hypothesis. To unify these considerations, a new quantity called the likelihood-generating function is defined. It is shown that all moments of both the likelihood and the log likelihood under both hypotheses can be derived from this one function. Moreover, the AUC can be expressed, to an excellent approximation, in terms of the likelihood-generating function evaluated at the origin. This expression is the leading term in an asymptotic expansion of the AUC; it is exact whenever the likelihood-generating function behaves linearly near the origin. It is also shown that the likelihood-generating function at the origin sets a lower bound on the AUC in all cases. # 1998 Optical Society of America [S0740-3232(98)02106-1] OCIS codes: 110.3000,110.4280,110.5010.
机译:我们继续先前论文的主题[J.选择。 Soc。上午。 A 7,1266(1990); 12,12,834(1995)]对图像质量进行客观(基于任务)评估。我们专注于信号检测任务和与ROC(接收机工作特性)曲线有关的品质因数。对于任意判别函数,使用ROC曲线下面积(AUC)的许多不同表达式都得到了推导,并对关于判别函数的哪些信息可用提供了不同的假设。特别地,示出了AUC可以由涉及判别式的特征函数的主值积分来表达。然后,讨论专门针对理想的观察者,定义为使用似然比(或似然比的某种单调变换,例如其对数)作为判别函数的人。理想观察者的属性是从第一原理开始的。得出了几率对似然比或对数似然矩的强约束,并且表明这些检验统计量的概率密度函数密切相关。尤其是,对于对数似然在一个假设下正态分布的情况,给出了一些令人惊讶的结果。为了统一这些考虑,定义了一个称为似然度生成函数的新量。结果表明,在两个假设下,似然和对数似然的所有矩都可以从这一函数导出。而且,AUC可以根据在原点评估的似然函数来很好地表示。该表达式是AUC渐近展开的主要术语。每当似然性生成函数在原点附近线性表现时,它都是精确的。还表明,在所有情况下,原点处的似然度生成函数都会在AUC上设置下限。 #1998美国光学学会[S0740-3232(98)02106-1] OCIS代码:110.3000,110.4280,110.5010。

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