Omnidirectional　image　(OI)　quality　assessment　is　crucial　to　facilitate　the　development　of　virtual　reality　(VR)　related　technology.　In　this　work,　a　distortion　discrimination　assisted　multi-stream　network　is　proposed　for　OI　quality　assessment.　The　multi-stream　architecture　is　constructed　by　generating　the　viewport　images　received　by　the　retina　at　one　point　to　simulate　the　characteristics　of　humans　perceiving　VR　contents.　Additionally,　the　strategy　of　generating　several　viewport　image　sets　from　one　OI　is　proposed　for　data　augmentation.　Furthermore,　the　facts　that　the　human　brain　has　the　ability　for　both　quality　assessment　and　distortion　type　distinguishment,　and　the　process　of　human　brain　handling　two　tasks　exists　information　interaction　inspire　us　to　employ　an　auxiliary　distortion　discrimination　task　to　facilitate　the　quality　assessment　task　learning.　Extensive　experiments　conducted　on　two　public　OI　databases　demonstrate　the　superiority　of　the　proposed　method　to　both　traditional　2D　quality　metrics　and　existing　metrics　specific　for　OIs.　Moreover,　utilizing　the　assistant　task　is　proven　to　be　more　effective　than　the　single　task　learning　for　OI　quality　evaluation.　Better　generalization　performance　is　also　verified　to　be　another　valuable　trait　of　the　proposed　method.