The　limited　bandwidth　of　underwater　acoustic　channels　poses　a　challenge　to　the　efficiency　of　multimedia　information　transmission.　To　improve　efficiency,　the　system　aims　to　transmit　less　data　while　maintaining　image　utility　at　the　receiving　end.　Although　assessing　utility　within　compressed　information　is　essential,　the　current　methods　exhibit　limitations　in　addressing　utility-driven　quality　assessment.　Therefore,　this　letter　built　a　Utility-oriented　compacted　Image　Quality　Dataset　(UCIQD)　that　contains　utility　qualities　of　reference　images　and　their　corresponding　compcated　information　at　different　levels.　The　utility　score　is　derived　from　the　average　confidence　of　various　object　detection　models.　Then,　based　on　UCIQD,　we　introduce　a　Distillation-based　Compacted　Information　Quality　assessment　metric　(DCIQ)　for　utility-oriented　quality　evaluation　in　the　context　of　underwater　machine　vision.　In　DCIQ,　utility　features　of　compacted　information　are　acquired　through　transfer　learning　and　mapped　using　a　Transformer.　Besides,　we　propose　a　utility-oriented　cross-model　feature　fusion　mechanism　to　address　different　detection　algorithm　preferences.　After　that,　a　utility-oriented　feature　quality　measure　assesses　compacted　feature　utility.　Finally,　we　utilize　distillation　to　compress　the　model　by　reducing　its　parameters　by　55%.　Experiment　results　effectively　demonstrate　that　our　proposed　DCIQ　can　predict　utility-oriented　quality　within　compressed　underwater　information.