In　sonar　applications,　important　information　such　as　distributions　of　minerals,　underwater　creatures　has　a　high　probability　of　being　contained　in　sonar　images.　In　many　underwater　applications　such　as　underwater　rescue　and　biometric　tracking,　it　is　necessary　to　send　sonar　images　underwater　for　further　analysis.　Due　to　the　bad　conditions　of　underwater　acoustic　channel　and　current　underwater　acoustic　communication　technologies,　sonar　images　very　possibly　suffer　from　several　typical　types　of　distortions.　As　far　as　we　know,　limited　efforts　have　been　made　to　gather　meaningful　sonar　image　databases　and　benchmark　reliable　objective　quality　model,　so　far.　This　paper　develops　a　new　objective　sonar　image　quality　predictor　(SIQP),　whose　core　is　the　combination　of　two　features　specific　to　a　quality　measure　of　sonar　images.　These　two　features,　which　come　from　statistical　and　structural　information　inspired　by　the　characteristics　of　sonar　images　and　the　human　visual　system,　reflect　image　quality　from　the　global　and　detailed　aspects.　The　performance　comparison　of　the　proposed　metric　with　popular　and　prevailing　quality　evaluation　models　is　conducted　using　a　newly　established　sonar　image　quality　database.　The　results　of　experiments　show　the　superiority　of　our　SIQP　metric　over　the　available　quality　evaluation　models.