IEEE Trans Image Process. 2018 Apr;27(4):1600-1610. doi: 10.1109/TIP.2017.2781307.
In this paper, we propose a novel no reference quality assessment method by incorporating statistical luminance and texture features (NRLT) for screen content images (SCIs) with both local and global feature representation. The proposed method is designed inspired by the perceptual property of the human visual system (HVS) that the HVS is sensitive to luminance change and texture information for image perception. In the proposed method, we first calculate the luminance map through the local normalization, which is further used to extract the statistical luminance features in global scope. Second, inspired by existing studies from neuroscience that high-order derivatives can capture image texture, we adopt four filters with different directions to compute gradient maps from the luminance map. These gradient maps are then used to extract the second-order derivatives by local binary pattern. We further extract the texture feature by the histogram of high-order derivatives in global scope. Finally, support vector regression is applied to train the mapping function from quality-aware features to subjective ratings. Experimental results on the public large-scale SCI database show that the proposed NRLT can achieve better performance in predicting the visual quality of SCIs than relevant existing methods, even including some full reference visual quality assessment methods.
在本文中，我们提出了一种新的无参考质量评估方法，通过结合统计亮度和纹理特征（NRLT），对具有局部和全局特征表示的屏幕内容图像（SCIs）进行评估。该方法的设计灵感来自人类视觉系统（HVS）的感知特性，即 HVS 对图像感知中的亮度变化和纹理信息敏感。在该方法中，我们首先通过局部归一化计算亮度图，然后进一步在全局范围内提取统计亮度特征。其次，受神经科学中已有研究的启发，我们采用了四个具有不同方向的滤波器从亮度图中计算梯度图。这些梯度图随后用于通过局部二值模式提取二阶导数。然后，我们通过全局范围内的高阶导数直方图提取纹理特征。最后，支持向量回归被应用于训练从质量感知特征到主观评分的映射函数。在公共大规模 SCI 数据库上的实验结果表明，与相关的现有方法相比，所提出的 NRLT 可以在预测 SCIs 的视觉质量方面取得更好的性能，甚至包括一些全参考视觉质量评估方法。
