摘要:随着多客户端交互多媒体应用的快速发展，屏幕内容图像（screen content image，简称SCI）的分发和处理与日俱增.图像质量评价课题的研究是其他许多应用的基础，至今图像质量评价课题研究的重点是传统自然图像，因此，针对屏幕图像质量评价的研究就变得非常迫切和必要.客观图像质量评价算法提出的基础建立在标准图像质量评价数据库上.首先构建了一个大规模的屏幕内容图像质量评价数据库（immersive media laboratory screen content image quality database，简称IML-SCIQD）.IML-SCIQD数据库包含参考图像25幅以及经过10种失真处理的1 250幅失真图像.以建立的IML-SCIQD数据库为基础，考虑到屏幕内容图像的图像区域与文本区域的视觉感知差异，在基于自然场景统计的无参考方法的启发下，提出了针对屏幕内容图像的无参考评价算法（natural scene statistics based no reference screen content image quality assessment metric，简称NSNRS）.NSNRS算法首先分别计算图像区域和文本区域的质量分数，再将这两个区域的质量分数结合起来得到整幅失真图像的质量分数.该算法与其他12种经典的客观评价算法，包括全参考算法、部分参考算法与无参考算法，在IML-SCIQD数据库和SIQAD数据库上进行了性能测试和对比，结果表明，所提出的算法优于经典的无参考评价算法；就整个数据库而言，所提出的算法可以达到与全参考方法相当的性能.



Abstract:With the rapid development of multi-device interactive applications, the transmission and processing of screen content image (SCI) is growing every day. Image quality assessment, which is the basis of many other research topics, has mainly focused on traditional natural images so far. Image quality assessment specifically for screen content image is therefore becoming very important and urging. Considering that image quality assessment database is the basis of objective image quality assessment metrics, this paper first constructs a large scale Immersive Media Laboratory screen content image quality database (IML-SCIQD). The IML-SCIQD database contain 25 reference images and 1250 distorted images that are distorted by 10 distortions. Based on the IML-SCIQD database, the visual perception difference of pictorial region and textual region is studied. At the same time, inspired by the idea of natural scene statistics (NSS) based no reference (NR) image quality assessment metrics, a NSS based NR content image quality assessment metric (NSNRS) is proposed. The quality scores of textual region and pictorial region are first computed in the NSNRS metric. Then, the quality scores of these two regions are combined to get the quality score of the whole screen content image. For performance comparison, the proposed metric is compared with 12 state-of-the-art objective image quality assessment metrics, including full reference, reduced reference and no reference algorithms, on the IML-SCIQD database and the SIQAD database. Extensive experiments support that the proposed algorithm outperforms the existing representative no reference techniques, and that the new metric has comparable performance with those full reference metrics for the whole database.



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