In　submarine　and　underwater　detection　tasks,　conventional　optical　imaging　and　analysis　methods　are　not　universally　applicable　due　to　the　limited　penetration　depth　of　visible　light.　Instead,　sonar　imaging　has　become　a　preferred　alternative.　However,　the　capture　and　transmission　conditions　in　complicated　and　dynamic　underwater　environments　inevitably　lead　to　visual　quality　degradation　of　sonar　images,　which　might　also　impede　further　recognition,　analysis　and　understanding.　To　measure　this　quality　decrease　and　provide　a　solid　quality　indicator　for　sonar　image　enhancement,　we　propose　a　task-　and　perception-oriented　sonar　image　quality　assessment　(TPSIQA)　method,　in　which　a　semi-reference　(SR)　approach　is　applied　to　adapt　to　the　limited　bandwidth　of　underwater　communication　channels.　In　particular,　we　exploit　reduced　visual　features　that　are　critical　for　both　human　perception　of　and　object　recognition　in　sonar　images.　The　final　quality　indicator　is　obtained　through　ensemble　learning,　which　aggregates　an　optimal　subset　of　multiple　base　learners　to　achieve　both　high　accuracy　and　a　high　generalization　ability.　In　this　way,　we　are　able　to　develop　a　compact　but　generalized　quality　metric　using　a　small　database　of　sonar　images.　Experimental　results　demonstrate　competitive　performance,　high　efficiency,　and　strong　robustness　of　our　method　compared　to　the　latest　available　image　quality　metrics.