Quality　assessment　for　stitched　panoramic　images　(SPIQA)　is　of　great　significance　for　the　stitching　algorithm　optimization.　By　contrast,　this　task　is　much　more　challenging　and　arduous　than　traditional　IQA　task　due　to　the　high　resolution　of　stitched　panoramic　images　and　the　particularity　and　complexity　of　stitching　distortions.　For　this　task,　we　propose　an　effective　method　based　on　patch　registration　and　bidimensional　feature　aggregation　(PRBFA).　First,　inspired　by　the　attention　mechanism　of　the　human　visual　system　and　the　limited　range　of　human　vision,　a　soft　patch　segmentation　and　selection　method　is　presented　to　determine　the　key　patches　in　panoramic　images　to　participate　in　the　following　patch　matching　and　feature　alignment　stages,　achieving　patch　registration　between　the　panoramic　image　and　the　corresponding　constituent　images.　Further,　to　fully　simulate　the　human　visual　perception　process　from　local　viewport　to　panorama,　the　feature　exploration　is　successively　performed　from　local　to　global,　which　is　also　adaptive　to　the　complexity　of　the　distortions　in　stitched　panoramic　images.　For　performance　testification,　extensive　experiments　are　conducted　on　the　publicly　released　SPIQA　database,　the　results　of　which　prove　the　performance　superiority　of　the　PRBFA　method.