Due　to　the　light　absorption　and　scattering　in　waterbodies,　acquired　underwater　images　frequently　suffer　from　color　cast,　blur,　low　contrast,　noise,　etc.,　which　seriously　degrade　the　image　quality　and　affect　their　subsequent　applications.　Therefore,　it　is　necessary　to　propose　a　reliable　and　practical　underwater　image　quality　assessment　(IQA)　model　that　can　faithfully　evaluate　underwater　image　quality.　To　this　end,　in　this　article,　we　establish　a　novel　quality　assessment　model　for　underwater　images　by　in-depth　analysis　and　characterization　of　multiple　image　properties.　Specifically,　we　propose　characterizing　the　image　luminance,　color　cast,　sharpness,　contrast,　fog　density　and　noise　to　comprehensively　describe　the　image　quality　to　evaluate　the　underwater　image　quality　more　accurately.　Dedicated　features　are　elaborately　investigated　to　characterize　those　quality-aware　image　properties.　After　feature　extraction,　we　employ　support　vector　regression　(SVR)　to　integrate　all　the　quality-aware　features　and　regress　them　onto　the　underwater　image　quality　score.　Extensive　tests　performed　on　standard　underwater　image　quality　databases　demonstrate　the　superior　prediction　performance　of　the　proposed　underwater　IQA　model　to　state-of-the-art　congeneric　quality　assessment　models.