Universidade Federal do Rio de Janeiro, Rio de Janeiro, 21941-972, Brazil.
IEEE Trans Image Process. 2011 Jan;20(1):64-75. doi: 10.1109/TIP.2010.2053549.
In this paper, we address the problem of no-reference quality assessment for digital pictures corrupted with blur. We start with the generation of a large real image database containing pictures taken by human users in a variety of situations, and the conduction of subjective tests to generate the ground truth associated to those images. Based upon this ground truth, we select a number of high quality pictures and artificially degrade them with different intensities of simulated blur (gaussian and linear motion), totalling 6000 simulated blur images. We extensively evaluate the performance of state-of-the-art strategies for no-reference blur quantification in different blurring scenarios, and propose a paradigm for blur evaluation in which an effective method is pursued by combining several metrics and low-level image features. We test this paradigm by designing a no-reference quality assessment algorithm for blurred images which combines different metrics in a classifier based upon a neural network structure. Experimental results show that this leads to an improved performance that better reflects the images' ground truth. Finally, based upon the real image database, we show that the proposed method also outperforms other algorithms and metrics in realistic blur scenarios.
在本文中，我们解决了数字图像因模糊而受损的无参考质量评估问题。我们首先生成一个包含各种情况下用户拍摄的图像的大型真实图像数据库，并进行主观测试以生成与这些图像相关的真实数据。基于这个真实数据，我们选择了一些高质量的图片，并使用不同强度的模拟模糊（高斯和线性运动）对其进行人工降级，总共生成了 6000 张模拟模糊图像。我们在不同的模糊场景中广泛评估了最先进的无参考模糊量化策略的性能，并提出了一种模糊评估范例，通过结合多个度量标准和底层图像特征来追求有效的方法。我们通过设计一种基于神经网络结构的分类器，将不同的度量标准结合在一起，为模糊图像设计了一种无参考质量评估算法，以此来测试这个范例。实验结果表明，这可以提高性能，更好地反映图像的真实数据。最后，基于真实图像数据库，我们还表明，在现实的模糊场景中，所提出的方法也优于其他算法和度量标准。
