Existing no-reference (NR) image quality assessment (IQA) metrics are still not convincing for evaluating the quality of the camera-captured images. Toward tackling this issue, we, in this article, establish a novel NR quality metric for quantifying the quality of the camera-captured images reliably. Since the image quality is hierarchically perceived from the low-level preliminary visual perception to the high-level semantic comprehension in the human brain, in our proposed metric, we characterize the image quality by exploiting both the low-level image properties and the high-level semantics of the image. Specifically, we extract a series of low-level features to characterize the fundamental image properties, including the brightness, saturation, contrast, noiseness, sharpness, and naturalness, which are highly indicative of the camera-captured image quality. Correspondingly, the high-level features are designed to characterize the semantics of the image. The low-level and high-level perceptual features play complementary roles in measuring the image quality. To infer the image quality, we employ the support vector regression (SVR) to map all the informative features to a single quality score. Thorough tests conducted on two standard camera-captured image databases demonstrate the effectiveness of the proposed quality metric in assessing the image quality and its superiority over the state-of-the-art NR quality metrics. The source code of the proposed metric for camera-captured images is released at https://github.com/YT2015?tab=repositories.

中文翻译：

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为相机捕获的图像建立无参考质量指标


现有的无参考（NR）图像质量评估（IQA）指标对于评估相机捕获图像的质量仍然没有说服力。为了解决这个问题，我们在本文中建立了一种新颖的 NR 质量指标，用于可靠地量化相机捕获的图像的质量。由于图像质量是从低级初步视觉感知到人脑高级语义理解的分层感知，因此在我们提出的指标中，我们通过利用低级图像属性和高级图像属性来表征图像质量。图像的层次语义。具体来说，我们提取一系列低级特征来表征基本图像属性，包括亮度、饱和度、对比度、噪声、锐度和自然度，这些特征高度反映了相机捕获的图像质量。相应地，高级特征被设计来表征图像的语义。低级和高级感知特征在测量图像质量方面发挥着互补作用。为了推断图像质量，我们采用支持向量回归（SVR）将所有信息特征映射到单个质量分数。对两个标准相机捕获图像数据库进行的彻底测试证明了所提出的质量指标在评估图像质量方面的有效性及其相对于最先进的 NR 质量指标的优越性。相机捕获图像的拟议指标的源代码发布于 https://github.com/YT2015?tab=repositories。