In　the　past　few　decades,　many　attempts　have　been　maken　to　evaluate　the　image　quality　assessment　(IQA)　of　natural　scene　images.　However,　the　IQA　research　of　animation　images　(AIs)　has　been　highly　overlooked.　In　this　article,　we　carry　out　in-depth　study　on　perceptual　quality　assessment　of　AIs.　As　the　lack　of　a　public　and　diverse　testing　database　currently,　this　paper　builds　a　large-scale　Animation　Images　Quality　Assessment　Database　(AIQAD).　This　database　totally　includes　1050　distorted　images　derived　from　30　source　images　by　corrupting　seven　distortion　types　with　multiple　distortion　levels.　Then,　a　subjective　experiment,　which　is　the　basic　and　accurate　quality　evaluation　measurement,　is　conducted　to　obtain　the　mean　opinion　score　(MOS)　for　each　image.　Furthermore,　we　also　investigate　the　feasibility　of　utilizing　existing　mainstream　full　reference　(FR)　IQA　metrics　to　solve　the　IQA　problem　of　AIs.　Experimental　results　demonstrate　that　existing　mainstream　FR　IQA　metrics　merely　achieve　fair　performance　on　the　proposed　database.