In　recent　decades,　the　emergence　of　image　applications　has　greatly　facilitated　the　development　of　vision-based　tasks.　As　a　result,　image　quality　assessment　(IQA)　has　become　increasingly　significant　for　monitoring,　controlling,　and　improving　visual　signal　quality.　While　existing　IQA　methods　focus　on　image　fidelity　and　aesthetics　to　characterize　perceived　quality,　it　is　important　to　evaluate　the　utility-centered　quality　of　an　image　for　popular　tasks,　such　as　object　detection.　However,　research　shows　that　there　is　a　low　correlation　between　utilities　and　perceptions.　To　address　this　issue,　this　article　proposes　a　utility-centered　IQA　approach.　Specifically,　our　research　focuses　on　underwater　fish　detection　as　a　challenging　task　in　an　underwater　environment.　Based　on　this　task,　we　have　developed　a　utility-centered　underwater　image　quality　database　(UIQD)　and　a　transfer　learning-based　advanced　underwater　quality　by　utility　assessment　(AQUA).　Inspired　by　the　top-down　design　approach　used　in　fidelity-oriented　IQA　methods,　we　utilize　deep　models　of　object　detection　and　transfer　their　features　to　the　mission　of　utility-centered　quality　evaluation.　Experimental　results　validate　that　the　proposed　AQUA　achieves　promising　performance　not　only　in　fish　detection　but　also　in　other　tasks　such　as　face　recognition.　We　believe　that　our　research　provides　valuable　insights　to　bridge　the　gap　between　IQA　research　and　visual　tasks.