In　this　paper,　we　address　problems　associated　with　free-energy-principle-based　image　quality　assessment　(IQA)　algorithms　for　objectively　assessing　the　quality　of　Screen　Content　(SC)　and　three-dimensional　(3-D)　synthesized　images　and　also　propose　a　very　fast　and　efficient　IQA　algorithm　to　address　these　issues.　These　algorithms　separate　an　image　into　predicted　and　disorder　residual　parts　and　assume　disorder　residual　part　does　not　contribute　much　to　the　overall　perceptual　quality.　These　algorithms　fail　for　quality　estimation　of　SC　images　as　information　of　textual　regions　in　SC　images　are　largely　separated　into　the　disorder　residual　part　and　less　information　in　the　predicted　part　and　subsequently,　given　a　negligible　emphasis.　However,　this　is　in　contrast　with　the　characteristics　of　human　vision.　Since　our　eyes　are　well　trained　to　detect　text　in　daily　life.　So,　our　human　vision　has　prior　information　about　text　regions　and　can　sense　small　distortions　in　these　regions.　In　this　paper,　we　proposed　a　new　reduced-reference　IQA　algorithm　for　SC　images　based　upon　a　more　perceptually　relevant　prediction　model　and　distortion　categorization,　which　overcomes　problems　with　existing　free-energy-principle-based　predictors.　From　experiments,　it　is　validated　that　the　proposed　model　has　a　better　capability　of　efficiently　estimating　the　quality　of　SC　images　as　compared　to　the　recently　developed　reduced-reference　IQA　algorithms.　We　also　applied　the　proposed　algorithm　to　judge　the　quality　of　3-D　synthesized　images　and　observed　that　it　even　achieves　better　performance　than　the　full-reference　IQA　metrics　specifically　designed　for　the　3-D　synthesized　views.