Sonar　technology　has　been　widely　used　in　underwater　surface　mapping　and　remote　object　detection　for　its　light-independent　characteristics.　Recently,　the　booming　of　artificial　intelligence　further　surges　sonar　image　(SI)　processing　and　understanding　techniques.　However,　the　intricate　marine　environments　and　diverse　nonlinear　postprocessing　operations　may　degrade　the　quality　of　SIs,　impeding　accurate　interpretation　of　underwater　information.　Efficient　image　quality　assessment　(IQA)　methods　are　crucial　for　quality　monitoring　in　sonar　imaging　and　processing.　Existing　IQA　methods　overlook　the　unique　characteristics　of　SIs　or　focus　solely　on　typical　distortions　in　specific　scenarios,　which　limits　their　generalization　capability.　In　this　article,　we　propose　a　unified　sonar　IQA　method,　which　overcomes　the　challenges　posed　by　diverse　distortions.　Though　degradation　conditions　are　changeable,　ideal　SIs　consistently　require　certain　properties　that　must　be　task-centered　and　exhibit　attribute　consistency.　We　derive　a　comprehensive　set　of　quality　attributes　from　both　the　task　background　and　visual　content　of　SIs.　These　attribute　features　are　represented　in　just　ten　dimensions　and　ultimately　mapped　to　the　quality　score.　To　validate　the　effectiveness　of　our　method,　we　construct　the　first　comprehensive　SI　dataset.　Experimental　results　demonstrate　the　superior　performance　and　robustness　of　the　proposed　method.