Images　are　vulnerable　to　different　kinds　of　distortions,　such　as　blur,　noise,　blockiness　etc,　which　all　degrade　the　image　quality.　Among　the　distorted　images,　out-of-focus　blurred　images　are　frequently　encountered　and　occupy　a　large　proportion.　However,　few　efforts　have　been　done　to　quality　evaluation　for　these　images.　In　this　paper,　we　devise　a　dedicated　quality　evaluation　scheme　to　automatically　infer　the　quality　of　out-of-focus　blurred　images,　which　is　named　GPSQ　(Gradient　magnitude　and　Phase　congruency-based　and　Saliency-guided　Quality　model).　In　GPSQ　a　pair　of　low-level　features,　including　gradient　magnitude　(GM)　and　phase　congruency　(PC),　are　extracted　to　characterize　the　image　local　blurriness.　Then　saliency　detection　is　performed　on　the　image　to　generate　a　corresponding　saliency　map.　Finally,　we　weight　the　local　structure　map　with　the　saliency　map　to　estimate　the　visual　quality　of　the　out-of-focus　blurred　image.　Experimental　results　demonstrate　the　proposed　GPSQ　delivers　high　consistency　with　subjective　evaluation　results.　(C)　2017　Elsevier　Inc.　All　rights　reserved.