The　compelling　creative　capabilities　of　stereo　video　have　captured　the　attention　of　scholars　towards　its　quality.　Given　the　substantial　challenge　posed　by　asymmetric　distortion　in　stereoscopic　visual　perception　within　the　realm　of　stereoscopic　video　quality　evaluation　(SVQA),　this　study　introduces　the　novel　D3Net　(Dual　Branch,　dual-stage　Attention,　Dual　Task)　framework　for　stereoscopic　video　quality　assessment.　Leveraging　its　innovative　dual-task　architecture,　D3Net　employs　a　dual-branch　independent　prediction　mechanism　for　the　left　and　right　views.　This　approach　not　only　effectively　addresses　the　prevalent　issue　of　asymmetric　distortion　in　stereoscopic　videos　but　also　pinpoints　which　view　drags　the　overall　score　down.　To　surmount　the　limitations　of　existing　models　in　capturing　global　detail　attention,　D3Net　incorporates　a　two-stage　distorted　attention　fusion　module.　This　module　enables　multi-level　fusion　of　video　features　at　both　block　and　pixel　levels,　bolstering　the　model＇s　attention　towards　global　details　and　its　processing　capabilities,　consequently　enhancing　the　overall　performance　of　the　model.　D3Net　has　exhibited　exceptional　performance　across　mainstream　and　cross-domain　datasets,　establishing　itself　as　the　current　state-of-the-art　(SOTA)　technology.　©　2025　Elsevier　B.V.