An　ideal　quality　assessment　model　should　simulate　the　properties　of　the　visual　brain　to　be　consistent　with　human　evaluation.　The　visual　brain　appears　to　have　both　evolved　to　seek　an　efficient,　decorrelated　representation　of　image　information　and　to　＂match＂　the　statistics　of　the　natural　image.　On　one　hand,　the　theoretical　studies　suggest　that　sparse　representation　resembles　the　strategy　in　the　primary　visual　cortex　of　brain　for　representing　natural　images.　On　the　other　hand,　the　natural　scene　statistics　have　driven　the　evolution　of　human　visual　system　and　have　also　inspired　the　understanding　and　simulating　of　visual　perception.　Inspired　by　these　observations,　in　this　paper,　we　propose　a　novel　reduced-reference　stereoscopic　image　quality　assessment　metric　using　sparse　representation　and　natural　scene　statistics　to　simulate　the　visual　perception　of　the　brain.　Specifically,　the　distribution　statistics　of　the　classified　visual　primitives　extracted　by　sparse　representation　are　used　to　measure　the　visual　information,　which　is　closely　related　to　the　hierarchical　progressive　process　of　human　visual　perception.　Particularly,　the　mutual　information　of　classified　primitives　between　two　view　images　is　derived　as　a　binocular　cue　to　simulate　the　binocular　fusion　process.　The　maximum　mechanism　that　is　applied　to　select　the　visual　information　is　a　pooling　mechanism　with　which　complex　cells　use　the　maximal　stimuli　from　a　group　of　simple　cells　during　the　transfer　process　in　the　primary　visual　cortex.　The　natural　scene　statistics　of　locally　normalized　luminance　coefficients　are　used　to　evaluate　the　natural　losses　due　to　the　presence　of　distortions.　The　differences　of　the　visual　information　and　the　natural　scene　statistics　between　the　original　and　distorted　images　are　used　to　compute　the　quality　score　by　a　prediction　function　which　is　trained　using　support　vector　regression.　Experimental　results　show　that　the　proposed　metric　outperforms　the　state-of-the-art　stereoscopic　image　quality　assessment　metrics　on　LIVE　3D　IQA　database　and　NBU-MDSID　Phase-II　database,　and　delivers　competitive　performance　on　Waterloo　IVC　3D　database.