Nowadays,　image　retargeting　approaches　have　been　widely　applied　to　adapt　images　of　various　resolutions　to　heterogenous　display　devices.　To　assess　the　quality　of　the　retargeted　images,　image　retargeting　quality　assessment　(IRQA)　has　emerged　as　a　critical　problem　in　image　quality　assessment.　In　this　paper,　we　address　the　IRQA　problem　with　a　newly　proposed　framework　based　on　registration　confidence　measurement　(RCM)　and　noticeability-based　pooling　(NBP).　First,　we　define　the　RCM　to　evaluate　the　accuracy　of　image　registration,　which　aligns　scenes　between　the　original　and　retargeted　images.　We　then　integrate　the　proposed　RCM　with　the　computed　local　fidelity　of　each　image　block　to　alleviate　the　negative　influence　of　inaccurate　registration　on　fidelity　measurements.　Meanwhile,　we　present　a　visual　attention　fusion　(VAF)　framework　to　enhance　faces　and　lines　in　the　saliency　map,　which　are　observed　to　be　highly　sensitive　in　the　human　visual　system　(HVS).　Finally,　we　propose　the　NBP　strategy,　which　aggregates　the　local　fidelity　of　each　image　block　into　the　overall　quality　of　the　retargeted　image.　Specifically,　the　NBP　strategy　sets　larger　quality　ranges　for　the　regions　where　the　visual　distortions　are　more　accessible　to　HVS　to　reflect　the　easy　noticeability　of　these　regions.　Experimental　results　on　the　MIT　RetargetMe　and　CUHK　datasets　demonstrate　that　the　proposed　IRQA　metric　based　on　RCM　and　NBP　outperforms　the　state-of-the-art　IRQA　metrics.