In　the　era　of　media　information　explosion,　there　is　an　urgent　need　for　a　fast　and　reliable　image　quality　assessment　(IQA)　model　to　improve　the　actual　application　effect　of　images.　To　this　end,　we　propose　the　multiple　information　measurement　fusion　metric　(MMFM),　which　innovatively　combines　two　types　of　information　measures　(IMs),　i.e.,　local　IM　and　global　IM,　using　only　a　small　number　of　references　for　IQA.　First,　inspired　by　the　free　energy　theory,　we　combine　2-dimensional　autoregressive　model　with　sparse　random　sampling　method　as　an　inference　engine　on　an　input　image　to　generate　its　associated　predicted　image.　Second,　by　the　inspiration　of　pixel-wise　measurement,　we　obtain　the　local　IM　by　calculating　the　information　entropy　of　the　residual　error　between　the　input　image　and　its　corresponding　predicted　one.　Third,　motivated　by　the　histogram-based　measurement,　we　acquire　the　global　IM　by　computing　the　two　kinds　of　divergences　between　the　input　image　and　its　predicted　one.　Fourth,　we　systematically　fuse　three　components,　independently　including　one　distance　of　local　IM　between　the　reference　and　corrupted　images　and　two　distances　of　global　IMs　between　the　reference　and　corrupted　images,　based　on　a　linear　function　to　derive　the　final　IQA　result.　The　results　of　experiment　on　the　most　popular　LIVE　database　show　that　our　designed　algorithm　with　only　one　number　used　as　few　reference　has　achieved　well　performance　as　compared　with　several　mainstream　IQA　models.　©　2023,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.