A　screen　content　image　(SCI)　has　great　difference　compared　with　a　natural　image,　and　an　SCI　contains　more　text,　graphic,　and　special　layout.　Considering　the　influences　of　texts,　graphics,　pictures,　and　layout　on　quality　of　an　SCI,　a　blind　quality　assessment　metric　for　SCIs　based　on　edge　and　structure　(BES)　has　been　proposed.　Since　texts,　graphics,　and　pictures　have　a　large　number　of　edges　and　the　human　visual　system　is　highly　sensitive　to　edges,　the　BES　metric　first　extracts　edges　using　the　imaginary　part　of　the　Gabor　filter　and　computes　an　edge　feature　for　each　SCI.　Second,　a　structure　feature　is　extracted　to　represent　the　layout　of　an　SCI.　Specifically,　the　Scharr　filter　is　exploited　to　calculate　a　local　binary　pattern　(LBP)　map　which　is　used　to　compute　a　structure　feature.　Finally,　a　random　forest　regression　algorithm　is　applied　to　map　the　edge　and　structure　features　to　subjective　scores.　The　experimental　results　show　that　in　the　database　SIQAD　and　SCID,　the　Pearson　linear　correlation　coefficient　(PLCC)　of　the　performance　of　the　proposed　BES　index　is　2.63%　and　11.22%　higher　than　the　latest　no　reference　index　in　the　comparison　respectively,　and　even　higher　than　some　full　reference　indexes.　©　2019,　Editorial　Board　of　JBUAA.　All　right　reserved.