Massive　content　composed　of　both　natural　scene　and　screen　content　has　been　generated　with　the　increasing　use　of　wireless　computing　and　cloud　computing,　which　call　for　general　image　quality　assessment　(IQA)　measures　working　for　both　natural　scene　images　(NSIs)　and　screen　content　images　(SCIs).　In　this　paper,　we　develop　a　saliency-induced　reduced-reference　(SIRR)　IQA　measure　for　both　NSIs　and　SCIs.　Image　quality　and　visual　saliency　are　two　widely　studied　and　closely　related　research　topics.　Traditionally,　visual　saliency　is　often　used　as　a　weighting　map　in　the　final　pooling　stage　of　IQA.　Instead,　we　detect　visual　saliency　as　a　quality　feature　since　different　types　and　levels　of　degradation　can　strongly　influence　saliency　detection.　Image　quality　is　described　by　the　similarity　between　two　images＇　saliency　maps.　In　SIRR,　saliency　is　detected　through　a　binary　image　descriptor　called　＂image　signature＂,　which　significantly　reduces　the　reference　data.　We　perform　extensive　experiments　on　five　large-scale　NSI　quality　assessment　databases　including　LIVE,　TID2008,　CSIQ　LIVEMD,　CID2013,　as　well　as　two　recently　constructed　SCI　QA　databases,　i.e.,　SIQAD　and　QACS.　Experimental　results　show　that　SIRR　is　comparable　to　state-of-the-art　full-reference　and　reduced-reference　IQA　measures　in　NSIs,　and　it　can　outperform　most　competitors　in　SCIs.　The　most　important　is　that　SIRR　is　a　cross-content-type　measure,　which　works　efficiently　for　both　NSIs　and　SCIs.　The　MATLAB　source　code　of　SIRR　will　be　publicly　available　with　this　paper.　(C)　2017　Elsevier　B.V.　All　rights　reserved.