Recent　years　have　witnessed　a　growing　number　of　image　and　video　centric　applications　on　mobile,　vehicular,　and　cloud　platforms,　involving　a　wide　variety　of　digital　screen　content　images.　Unlike　natural　scene　images　captured　with　modern　high　fidelity　cameras,　screen　content　images　are　typically　composed　of　fewer　colors,　simpler　shapes,　and　a　larger　frequency　of　thin　lines.　In　this　paper,　we　develop　a　novel　blind/no-reference　(NR)　model　for　accessing　the　perceptual　quality　of　screen　content　pictures　with　big　data　learning.　The　new　model　extracts　four　types　of　features　descriptive　of　the　picture　complexity,　of　screen　content　statistics,　of　global　brightness　quality,　and　of　the　sharpness　of　details.　Comparative　experiments　verify　the　efficacy　of　the　new　model　as　compared　with　existing　relevant　blind　picture　quality　assessment　algorithms　applied　on　screen　content　image　databases.　A　regression　module　is　trained　on　a　considerable　number　of　training　samples　labeled　with　objective　visual　quality　predictions　delivered　by　a　high-performance　full-reference　method　designed　for　screen　content　image　quality　assessment　(IQA).　This　results　in　an　opinion-unaware　NR　blind　screen　content　IQA　algorithm.　Our　proposed　model　delivers　computational　efficiency　and　promising　performance.　The　source　code　of　the　new　model　will　be　available　at:　https://sites.google.com/site/guke198701/publications.