Quality　assessment　of　stereoscopic　3D　images　is　much　more　complex　than　that　of　2D　images.　Quality　assessment　metrics　for　3D　images　have　achieved　good　performance　on　symmetric　distorted　3D　images,　while　given　poor　performance　on　asymmetric　distorted　3D　images.　To　further　improve　the　perception　consistency　for　symmetric　and　asymmetric　distorted　3D　images,　this　paper　presents　a　machine　learning-based　full-reference　3D　image　quality　assessment　method　that　learns　from　multi　IQA　metrics.　Considering　that　changes　in　left　view,　right　view　and　depth　information　all　impact　human　perception,　our　method　considers　the　quality　of　two　views　and　depth　information.　Since　symmetric　and　asymmetric　distortions　impact　left　and　right　views　of　a　3D　image　differently,　we　propose　different　features　for　symmetric　and　asymmetric　distorted　3D　images.　The　experimental　results　show　that　our　method　has　better　performance　than　both　2D-based　3D　quality　assessment　metrics　and　the　state　of　the　art　specially　designed　3D　quality　assessment　metrics.　©　2016　IEEE.