With　the　growing　popularity　of　portable　electronic　devices,　such　as　portable　computer　and　cellular　phone,　a　wide　variety　of　digital　screen　content　images　(SCIs)　have　drastically　invaded　into　our　daily　lives.　Unlike　natural　scene　images,　SCIs　are　typically　composed　of　graphic　and　textual　images,　with　simpler　shapes,　and　a　larger　frequency　of　thin　lines,　which　may　lead　to　different　viewing　experience.　Therefore,　an　accurate　quality　metric　for　SCIs　which　could　take　into　account　its　special　properties　is　of　particular　interest.　In　this　paper,　we　propose　a　novel　reduced-reference　method　for　assessing　the　perceptual　quality　of　SCIs.　Specifically,　the　principle　of　free　energy　models　the　perception　and　understanding　of　images　as　an　active　reasoning　process,　in　which　the　brain　attempts　to　explain　the　visual　scene　with　an　internal　generative　model.　Sparse　primitive　cues　are　explored　to　model　the　human　perception　of　the　visual　scene　taking　account　of　the　unique　properties　of　SCIs　and　the　structure　of　primitives　(atoms　in　the　dictionary).　The　difference　of　the　prediction　discrepancies　between　the　pristine　and　distorted　images　is　defined　as　a　measurement　of　the　image　quality.　Experimental　results　show　the　effectiveness　of　the　proposed　metric　and　it　performs　favorably　against　state-of-the-arts　on　the　benchmark　screen　image　quality　assessment　database.　©　2022,　Springer　Nature　Singapore　Pte　Ltd.