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.