Image　quality　assessment　(IQA)　has　always　been　an　active　research　topic　since　the　birth　of　the　digital　image.　Actually,　the　arrival　of　deep　learning　has　made　IQA　more　promising.　However,　most　state-of-the-art　no-reference　(NR)　IQA　methods　require　regression　training　on　distorted　images　or　extracted　features　with　subjective　image　scores,　which　makes　them　suffer　from　insufficient　reference　image　content　and　training　samples　with　subjectively　scoring　due　to　timeconsuming　and　laborious　subjective　testing.　Furthermore,　most　convolutional　neural　networks　(CNN)-based　methods　generally　transform　original　images　into　patches　to　accommodate　fixed-size　input　of　CNN,　which　often　alter　the　image＇s　data　and　introduce　noise　into　the　neural　network.　This　paper　aims　to　solve　the　above　problems　by　adopting　new　strategies　and　proposes　a　novel　NRIQA　method　based　on　deep　CNN.　Specifically,　first,　we　obtain　image　data　with　diverse　image　content,　multiple　image　sizes,　and　reasonable　distortion　by　crawling,　filtrating,　and　degrading　numerous　publicly　licensed　high-quality　images　from　the　Internet.　Then,　we　score　all　the　images　using　an　excellent　full-reference　(FR)　IQA　algorithm,　thereby　artificially　construct　a　large　objective　IQA　database.　Next,　we　design　a　deep　CNN,　which　can　accept　input　images　of　original　sizes　from　our　database　instead　of　patches,　then　we　train　the　model　with　the　FRIQA　index　as　training　objective　thus　propose　the　opinionunaware(OU)　NRIQA　method.　Finally,　the　experiment　results　show　that　our　method　achieves　excellent　performance,　which　outperforms　state-of-the-art　OU-NRIQA　models　and　is　comparable　to　most　of　the　traditional　opinion-aware　NRIQA　methods,　even　some　FRIQA　methods　on　standard　subjective　IQA　databases.　©　2019　SPIE.