With　the　rapid　development　of　multimedia　interactive　applications,　the　processing　volume　of　the　screen　content　(SC)　images　is　increasing　day　by　day.　The　research　on　image　quality　assessment　is　the　basis　of　many　other　applications.　The　focus　of　general　image　quality　assessment　(QA)　research　is　natural　scene　(NS)　images,　now　for　the　quality　assessment　research　of　SC　images　becomes　very　urgent　and　has　received　more　and　more　attention.　Accurate　quality　assessment　of　SC　images　helps　improve　the　user　experience.　Based　on　these,　this　paper　proposes　an　improved　method　using　very　sparse　reference　information　for　accurate　quality　assessment　of　SC　images.　Specifically,　the　proposed　method　extracts　macroscopic,　microscopic　structure　and　color　information　respectively,　and　measures　the　differences　in　terms　of　macroscopic,　microscopic　features　and　color　information　between　the　original　SC　image　and　its　distorted　version,　and　finally　calculates　the　overall　quality　score　of　the　distorted　SC　image.　The　quality　assessment　model　we　built　uses　a　dimension　reduction　histogram　and　only　needs　to　transmit　very　sparse　reference　information.　Experiments　show　that　the　proposed　method　has　obvious　superiority　over　the　state-of-the-art　relevant　quality　metrics　in　the　visual　quality　assessment　of　SC　images.　©　2020,　Springer　Nature　Singapore　Pte　Ltd.