Blind image quality assessment through anisotropy
Blind image quality assessment through anisotropy
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DOI：
10.1364/josaa.24.000b42
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发表时间：
2007-12-01
影响因子：
1.9
通讯作者：
Cristobal, Gabriel
中科院分区：
文献类型：
--
作者：
Gabarda, Salvador;Cristobal, Gabriel
我们描述了一种确定数字图像质量的创新方法。该方法基于测量给定图像在一组预定义方向上的预期熵的方差。可以通过使用空间/空间频率分布作为概率密度函数的近似来在局部基础上计算熵。为此选择了广义 Renyl 熵和归一化伪维格纳分布 (PWD)。由此，可以计算出逐像素的熵值，从而也可以生成熵直方图。预期熵的方差被测量为方向性的函数，并且它被视为各向异性指标。为此，可以通过使用定向一维 PWD 实现来实现方向选择性。我们的主要目的是展示如何使用这种各向异性测量作为评估图像保真度和质量的指标。实验结果表明，这样的指数呈现出一些类似于理想图像质量函数的理想特征，构成了自然图像的合适质量指数。也就是说，与其他退化、模糊或噪声版本相比，对焦、无噪声的自然图像已显示出该指标的最大值。这一结果提供了一种从其他降级版本中识别对焦、无噪声图像的方法，允许根据图像的相对质量对图像进行自动和非参考分类。还表明，新的测量方法与峰值信噪比等经典参考指标具有良好的相关性。 (C) 2007 美国光学学会
We describe an innovative methodology for determining the quality of digital images. The method is based on measuring the variance of the expected entropy of a given image upon a set of predefined directions. Entropy can be calculated on a local basis by using a spatial/spatial-frequency distribution as an approximation for a probability density function. The generalized Renyl entropy and the normalized pseudo-Wigner distribution (PWD) have been selected for this purpose. As a consequence, a pixel-by-pixel entropy value can be calculated, and therefore entropy histograms can be generated as well. The variance of the expected entropy is measured as a function of the directionality, and it has been taken as an anisotropy indicator. For this purpose, directional selectivity can be attained by using an oriented 1-D PWD implementation. Our main purpose is to show how such an anisotropy measure can be used as a metric to assess both the fidelity and quality of images. Experimental results show that an index such as this presents some desirable features that resemble those from an ideal image quality function, constituting a suitable quality index for natural images. Namely, in-focus, noise-free natural images have shown a maximum of this metric in comparison with other degraded, blurred, or noisy versions. This result provides a way of identifying in-focus, noise-free images from other degraded versions, allowing an automatic and nonreference classification of images according to their relative quality It is also shown that the new measure is well correlated with classical reference metrics such as the peak signal-to-noise ratio. (C) 2007 Optical Society of America