Because　the　pairwise　comparison　is　a　natural　and　effective　way　to　obtain　subjective　image　quality　scores,　we　propose　an　objective　full-reference　image　quality　assessment　(FR-IQA)　index　based　on　pairwise　learning　to　rank　(PLR).　We　first　compose　a　large　number　of　pairs　of　images,　extract　their　features,　and　compute　their　preference　labels　as　training　labels.　We　then　obtain　a　pairwise　preference　model　by　training　a　binary　classifier　using　the　features　and　labels.　Because　image　quality　is　affected　by　the　masking　effect,　we　propose　extracting　frequency-aware　quality　features　by　adapting　state-of-the-art　IQA　metrics.　The　learned　pairwise　preference　model　is　then　used　to　predict　the　preference　between　pairs　of　images　in　the　testing　dataset.　The　quality　of　each　image　is　computed　as　the　number　of　preferences.　Experimental　results　on　four　IQA　databases　validate　that　the　proposed　PLR-based　IQA　index　achieves　higher　consistency　with　human　subjective　evaluation　than　the　state-of-the-art　IQA　metrics.