One　challenge　facing　image　quality　assessment　(IQA)　is　that　current　models　designed　or　trained　on　the　basis　of　exiting　databases　are　intrinsically　suboptimal　and　cannot　deal　with　the　real-world　complexity　and　diversity　of　natural　scenes.　IQA　models　and　databases　are　heavily　skewed　toward　the　visibility　of　distortions.　It　is　critical　to　understand　the　wider　determinants　of　perceived　quality　and　use　the　new　understanding　to　improve　the　predictive　power　of　IQA　models.　Human　behavioral　categorization　performance　is　powerful　and　essential　for　visual　tasks.　However,　little　is　known　about　the　impact　of　natural　scene　categories　(SCs)　on　perceived　image　quality.　We　hypothesize　that　different　classes　of　natural　scenes　influence　image　quality　perception-how　image　quality　is　perceived　is　not　only　affected　by　the　lower　level　image　statistics　and　image　structures　shared　between　different　categories　but　also　by　the　semantic　distinctions　between　these　categories.　In　this　article,　we　first　design　and　conduct　a　fully　controlled　psychovisual　experiment　to　verify　our　hypothesis.　Then,　we　propose　a　computational　framework　that　integrates　the　natural　SC-specific　component　into　image　quality　prediction.　Research　demonstrates　the　importance　and　plausibility　of　considering　natural　SCs　in　future　IQA　databases　and　models.