Stereoscopic　images　typically　consist　of　left　and　right　views　along　with　depth　information.　Assessing　the　quality　of　stereoscopic/3D　images　(SIQA)　is　often　more　complex　than　that　of　2D　images　due　to　scene　disparities　between　the　left　and　right　views　and　the　intricate　process　of　fusion　in　binocular　vision.　To　address　the　problem　of　quality　prediction　bias　of　multi-distortion　images,　we　investigated　the　visual　physiology　and　the　processing　of　visual　information　by　the　primary　visual　cortex　of　the　human　brain　and　proposed　a　no-reference　stereoscopic　image　quality　evaluation　method.　The　method　mainly　includes　an　innovative　end-to-end　NR-SIQA　neural　network　with　a　picture　patch　generation　algorithm.　The　algorithm　generates　a　saliency　map　by　fusing　the　left　and　right　views　and　then　guides　the　image　cropping　in　the　database　based　on　the　saliency　map.　The　proposed　models　are　validated　and　compared　based　on　publicly　available　databases.　The　results　show　that　the　model　and　algorithm　together　outperform　the　state-of-the-art　NR-SIQA　metric　in　the　LIVE　3D　database　and　the　WIVC　3D　database,　and　have　excellent　results　in　the　specific　noise　metric.　The　model　generalization　experiments　demonstrate　a　certain　degree　of　generality　of　our　proposed　model.