Many　stereoscopic　image　retargeting　(SIR)　methods　have　been　developed　for　automatically　and　intelligently　resizing　stereoscopic　images　and　we　cannot　always　rely　on　time-consuming　subjective　user　studies　to　validate　the　performance　of　different　SIR　methods.　It　is　therefore　required　to　design　reliable　objective　metrics　for　SIR　quality　evaluation.　This　paper　extends　our　previous　2D　aspect　ratio　similarity　(ARS)　metric　to　a　stereo　3D　version　termed　as　StereoARS　where　the　key　idea　is　to　investigate　into　retargeting　inconsistency　between　the　original　stereo　correspondences.　Our　proposed　StereoARS　operates　via　two　stages:　monocular　quality　estimation　and　binocular　inconsistency　detection.　In　the　first　stage,　monocular　quality　estimation　is　performed　by　applying　a　modified　ARS　measure　on　the　left　and　right　views　separately　to　quantify　the　quality　degradation　within　each　monocular　view.　In　the　second　stage,　binocular　inconsistency　detection　is　performed　in　both　pixel-level　and　grid-level　to　characterize　the　influence　of　binocular　rivalry　and　stereo　visual　discomfort　on　SIR　quality.　In　addition,　we　also　measure　to　what　extent　the　original　pixel　visibility　relation　is　preserved　after　SIR　as　another　binocular　quality　factor.　Finally,　these　monocular　and　binocular　quality　estimates　are　fused　to　produce　an　overall　SIR　quality　score.　Extensive　experiments　have　demonstrated　that　StereoARS　achieves　better　alignment　with　human　subjective　ratings　than　the　existing　metrics　by　a　large　margin.