Image is a typical example of visual data, and its quality inevitably affects its application. Hence, measuring image quality accurately is a beneficial task. In practical application, there are different image types e.g. natural image and screen content image (SCIs). And the distortion types contained in images are various. Most image quality assessment (IQA) methods concentrate on a single image type with limited distortion types. In this paper, we present a no-reference IQA method which can accurately measure the quality for both natural image and SCI, and is robust for various distortion types. Human visual system is sensitive to the changes in image structural information which are usually caused by image quality degradation. Therefore, the new method employs local structural information representation for IQA. We first analyze the gray-scale fluctuation of each pixel in four detection directions to obtain four gray-scale fluctuation maps (GFMs) and one gray-scale fluctuation direction map (GFD). And then, the structural features extracted from GFMs and GFD are used for representing local structural information. Finally, the mapping function from the features to image subjective scores is trained by support vector regression (SVR). The experimental results on the public databases demonstrate that SVR is suitable for IQA and the proposed method can accurately predict the quality of both natural images and SCIs with various distortion types.

图像是视觉数据的典型示例，其质量不可避免地影响其应用。因此，准确测量图像质量是一项有益的任务。在实际应用中，存在不同的图像类型，例如自然图像和屏幕内容图像（SCI）。并且图像中包含的失真类型多种多样。大多数图像质量评估（IQA）方法都集中在具有有限失真类型的单个图像类型上。在本文中，我们提出了一种无参考的IQA方法，该方法可以准确地测量自然图像和SCI的质量，并且对于各种失真类型均具有鲁棒性。人类视觉系统对通常由图像质量下降引起的图像结构信息的变化敏感。因此，该新方法将局部结构信息表示用于IQA。我们首先分析每个像素在四个检测方向上的灰度波动，以获得四个灰度波动图（GFM）和一个灰度波动方向图（GFD）。然后，将从GFM和GFD中提取的结构特征用于表示局部结构信息。最后，通过支持向量回归（SVR）训练从特征到图像主观评分的映射功能。在公共数据库上的实验结果表明，SVR适用于IQA，并且所提出的方法可以准确预测具有各种失真类型的自然图像和SCI的质量。从GFM和GFD中提取的结构特征用于表示局部结构信息。最后，通过支持向量回归（SVR）训练从特征到图像主观评分的映射功能。在公共数据库上的实验结果表明，SVR适用于IQA，并且所提出的方法可以准确预测具有各种失真类型的自然图像和SCI的质量。从GFM和GFD中提取的结构特征用于表示局部结构信息。最后，通过支持向量回归（SVR）训练从特征到图像主观评分的映射功能。在公共数据库上的实验结果表明，SVR适用于IQA，并且所提出的方法可以准确地预测具有各种失真类型的自然图像和SCI的质量。