In　this　paper,　we　propose　a　novel　reduced-reference　(RR)　image　quality　assessment　(IQA)　algorithm　based　on　the　internal　generative　mechanism,　which　suggests　that　the　human　visual　system　(HVS)　can　actively　predict　the　primary　visual　information　and　avoid　the　uncertainty.　Specifically,　the　explanation　of　the　visual　scene　is　formulated　as　the　process　of　sparse　representation.　In　particular,　the　entropy　of　primitive　accounts　for　the　primary　visual　information　and　the　discrepancy　between　the　image　signal　and　its　best　sparse　description　is　regarded　as　the　uncertainty　in　perception.　As　such,　the　combined　feature　that　can　summarize　the　primary　visual　information　and　uncertainty　in　sparse　domain　is　required　to　be　transmitted　in　the　RR-IQA　framework.　Comparative　studies　of　the　proposed　reduced　reference　metric　is　conduced　on　both　single　and　multiple　distortion　databases,　and　experimental　results　demonstrate　that　the　proposed　metric　can　achieve　high　correlation　with　the　human　perception　by　only　sending　ignorable　additional　information.