Existing　NR-IIQA　(no　reference-based　inpainted　image　quality　assessment)　algorithms　assess　the　quality　of　an　inpainted　image　via　artificially　designed　unnaturalness　expression,　which　often　fail　to　capture　inpainted　artifacts.　This　paper　presents　a　new　deep　rank　learning-based　method　for　NR-IIQA.　The　model　adopts　a　siamese　deep　architecture,　which　takes　a　pair　of　inpainted　images　as　input　and　outputs　their　rank　order.　Each　branch　utilizes　a　CNN　structure　to　capture　the　global　structure　coherence　and　a　patch-wise　coherence　assessment　module　(PCAM)　to　depict　the　local　color　and　texture　consistency　in　an　inpainted　image.　To　train　the　deep　model,　we　construct　a　new　dataset,　which　contains　thousands　of　pairs　of　inpainted　images　with　ground-truth　quality　ranking　labels.　Rich　ablation　studies　are　conducted　to　verify　the　key　modules　of　the　proposed　architecture.　Comparative　experimental　results　demonstrate　that　our　method　outperforms　existing　NR-IIQA　metrics　in　evaluating　both　inpainted　images　and　inpainting　algorithms.