An information fidelity criterion for image quality assessment using natural scene statistics
An information fidelity criterion for image quality assessment using natural scene statistics
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DOI：
10.1109/tip.2005.859389
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发表时间：
2005-12-01
影响因子：
10.6
通讯作者：
de Veciana, G
中科院分区：
文献类型：
--
作者：
Sheikh, HR;Bovik, AC;de Veciana, G
视觉质量的测量对于众多图像和视频处理应用至关重要。质量评估（QA）研究的目标是设计能够以感知一致的方式自动评估图像或视频质量的算法。传统上，图像 QA 算法将图像质量解释为与某些感知空间中的“参考”或“完美”图像的保真度或相似度。这种“完全参考”的 QA 方法试图通过对人类视觉系统 (HVS) 的显着生理和心理视觉特征进行建模，或通过任意信号保真度标准来实现质量预测的一致性。在本文中，我们通过提出一种基于自然场景统计的新颖信息保真度标准来解决图像质量保证问题。 QA 系统总是涉及判断供“人类消费”的图像和视频的视觉质量。研究人员开发了复杂的模型来捕获自然信号的统计数据，即视觉环境的图片和视频。在信息论环境中使用这些统计模型，我们得出了一种新颖的 QA 算法，该算法比传统方法具有明显的优势。特别是，它是无参数的，并且在我们的测试中优于当前的方法。我们通过涉及 779 张图像的广泛主观研究来验证我们算法的性能。我们还表明，虽然我们的方法明显不同于传统的基于 HVS 的方法，但在某些条件下它在功能上与它们相似，但由于建模的改进，它的性能优于它们。主观研究的代码和数据可在 [1] 中找到。
Measurement of visual quality is of fundamental importance to numerous image and video processing applications. The goal of quality assessment (QA) research is to design algorithms that can automatically assess the quality of images or videos in a perceptually consistent manner. Traditionally, image QA algorithms interpret image quality as fidelity or similarity with a "reference" or "perfect" image in some perceptual space. Such "full-referene" QA methods attempt to achieve consistency in quality prediction by modeling salient physiological and psychovisual features of the human visual system (HVS), or by arbitrary signal fidelity criteria. In this paper, we approach the problem of image QA by proposing a novel information fidelity criterion that is based on natural scene statistics. QA systems are invariably involved with judging the visual quality of images and videos that are meant for "human consumption." Researchers have developed sophisticated models to capture the statistics of natural signals, that is, pictures and videos of the visual environment. Using these statistical models in an information-theoretic setting, we derive a novel QA algorithm that provides clear advantages over the traditional approaches. In particular, it is parameterless and outperforms current methods in our testing. We validate the performance of our algorithm with an extensive subjective study involving 779 images. We also show that, although our approach distinctly departs from traditional HVS-based methods, it is functionally similar to them under certain conditions, yet it outperforms them due to improved modeling. The code and the data from the subjective study are available at [1].