To　adapt　images　for　diversified　digital　devices,　researchers　have　presented　many　image　retargeting　methods.　However,　the　consistency　between　results　of　objective　image　retargeting　quality　assessment　(IRQA)　metrics　and　subjective　perception　is　still　low.　In　this　paper,　we　propose　a　visual　attention　fusion　(VAF)　framework　to　assist　IRQA　metrics　in　better　understanding　the　features　of　images　such　as　image　saliency,　faces,　and　lines.　First,　we　combine　the　results　of　multiple　salient　object　detection　algorithms　to　reduce　the　limitations　of　a　single　algorithm.　Second,　faces　and　lines　are　considered　in　our　framework　to　measure　deformations　to　these　visually　sensitive　regions.　Finally,　we　propose　a　saliency　enhancement　model　to　simulate　human　visual　attention　for　IRQA.　We　combine　the　proposed　VAF　framework　with　some　state-of-the-art　IRQA　metrics.　Experimental　results　show　that　the　proposed　VAF　framework　can　improve　the　consistency　between　the　results　of　objective　IRQA　metrics　and　subjective　opinion　scores.　©　Published　under　licence　by　IOP　Publishing　Ltd.