To　overcome　some　major　problems　with　traditional　saliency　evaluation　metrics,　full-reference　image　quality　assessment　(IQA)　metrics,　which　have　similar　but　stricter　objectives,　are　used.　Inspired　by　the　root　mean　absolute　error,　the　authors　propose　a　fitting-based　optimisation　method　for　salient　object　detection　algorithms.　Their　algorithm　analyses　the　quantitative　relationship　between　saliency　and　ground　truth　values,　and　uses　the　derived　relationship　to　fit　the　saliency　values　to　the　original　saliency　maps.　This　ensures　that　the　resulting　images,　which　are　composed　of　fitted　values,　are　closer　to　the　ground　truth.　The　proposed　algorithm　first　computes　the　statistics　of　the　ground　truth　and　saliency　maps　computed　by　each　salient　object　detection　algorithm.　These　statistics　are　used　to　compute　the　parameters　of　four　fitting　models,　which　generally　agree　with　the　characteristics　of　the　statistical　data.　For　a　new　saliency　map,　they　use　the　fitting　model　with　the　computed　parameters　to　obtain　the　fitted　saliency　values,　which　are　confined　to　the　range　[0,　255].　Finally,　they　evaluate　their　saliency　optimisation　algorithm　using　traditional　evaluation　metrics,　IQA　metrics,　and　a　content-based　image　retrieval　application.　The　results　show　that　the　proposed　approach　improves　the　quality　of　the　optimised　saliency　maps.