Currently,　visual　saliency　detection　has　been　discovered　and　widely　applied　in　the　field　of　computer　vision　and　image　processing.　Recent　works　have　shown　that　considering　human　visual　system　features　in　image　quality　assessment　will　improve　the　consistence　between　objective　assessment　results　and　subjective　visual　perception.　So　we　propose　saliency-aware　image　quality　assessment　through　a　universal　method　to　combine　saliency　detection　with　image　quality　assessment　metrics.　Since　there　is　no　image　dataset　used　for　image　quality　assessment　and　saliency　detection　researches　simultaneously,　we　create　a　new　image　database,　called　TID-2013S,　which　is　inheriting　from　image　quality　assessment　database　TID-2013　and　contains　25　manually　labeled　ground　truth　images.　In　this　paper,　we　select　8　representative　full　reference　image　quality　assessment　metrics　and　7　state-of-the-art　saliency　detection　algorithms　to　validate　the　performance　of　our　proposed　method.　Experiment　results　show　that　when　using　the　appropriate　parameter　of　mapping　function,　our　saliency-aware　approach　achieves　the　best　performance　improvement　by　5.41%.　And　for　most　of　image　quality　assessment　metrics,　the　MCA　saliency　detection　algorithm　works　best.