基于边缘和结构的无参考屏幕内容图像质量评估

屏幕内容图像（SCI）是一种与传统自然图像不同的图像，具有更多的文本、图形以及特殊的布局。考虑文本、图形、图像和布局对屏幕内容图像质量的影响，提出了针对屏幕内容图像的基于边缘和结构的无参考质量评估（BES）算法。文本、图形和图像具有大量边缘，并且人类视觉系统对边缘高度敏感，因此BES算法首先使用Gabor滤波器的虚部提取边缘并计算每张屏幕内容图像的边缘特征。其次，提取一个结构特征来表示屏幕内容图像的布局。具体而言，利用Scharr滤波器计算得到一个局部二值模式（LBP）图，接着利用LBP图计算得到结构特征。最后，应用随机森林回归算法将边缘和结构特征映射为主观分数。实验结果表明，在数据库SIQAD和SCID上，所提出BES算法性能的皮尔森线性相关系数（PLCC）相对于对比算法中最先进的无参考算法，分别提高了2.63%和11.22%，甚至高于一些全参考算法。 

Blind quality assessment for screen content images based on edge and structure

A screen content image (SCI) has great difference compared with a natural image, and an SCI contains more text, graphic, and special layout. Considering the influences of texts, graphics, pictures, and layout on quality of an SCI, a blind quality assessment metric for SCIs based on edge and structure (BES) has been proposed. Since texts, graphics, and pictures have a large number of edges and the human visual system is highly sensitive to edges, the BES metric first extracts edges using the imaginary part of the Gabor filter and computes an edge feature for each SCI. Second, a structure feature is extracted to represent the layout of an SCI. Specifically, the Scharr filter is exploited to calculate a local binary pattern (LBP) map which is used to compute a structure feature. Finally, a random forest regression algorithm is applied to map the edge and structure features to subjective scores. The experimental results show that in the database SIQAD and SCID, the Pearson linear correlation coefficient (PLCC) of the performance of the proposed BES index is 2.63% and 11.22% higher than the latest no reference index in the comparison respectively, and even higher than some full reference indexes.