Recently,　several　no-reference　image　quality　assessment　(NR-IQA)　metrics　have　been　developed　for　the　quality　evaluation　of　screen　content　images　(SCIs).　While,　most　of　them　are　opinion-aware　methods,　which　are　limited　by　the　subjective　opinion　scores　of　training　data.　Hence,　in　this　paper,　we　propose　a　novel　opinion-unaware　method　to　predict　the　quality　of　SCIs　without　any　prior　information.　Firstly,　an　union　feature　is　proposed　by　considering　the　local　and　global　visual　characteristics　of　human　visual　system　simultaneously.　Specifically,　a　local　structural　feature　is　extracted　from　the　rough　and　smooth　regions　of　SCIs　by　leveraging　a　sparse　representation　model.　As　a　supplement,　a　global　feature　is　obtained　by　combining　the　luminance　statistical　feature　and　local　binary　pattern　(LBP)　feature　of　entire　SCIs.　Secondly,　to　get　rid　of　the　limitation　of　subjective　opinion　scores,　a　new　large-scale　training　dataset　contained　80,000　distorted　SCIs　is　constructed,　and　the　quality　labels　of　those　distorted　SCIs　are　derived　by　an　advanced　full-reference　IQA　metric.　Thirdly,　a　regression　model　between　image　features　and　image　quality　labels　is　learned　from　the　training　dataset　by　employing　a　learning-based　framework.　And　then,　the　quality　scores　of　test　SCIs　can　be　predicted　by　the　pre-trained　regression　model.　The　experimental　results　on　two　largest　SCI-oriented　databases　show　that　the　proposed　method　is　superior　to　the　state-of-the-art　NR-IQA　metrics.　(C)　2018　Elsevier　Inc.　All　rights　reserved.