In　this　paper,　we　put　forward　an　effective　and　efficient　no　reference　image　blurriness　assessment　metric　on　the　basis　of　local　binary　pattern　(LBP)　features.　In　this　proposal,　we　reveal　that　part　of　the　LBP　histogram　bins　present　monotonously　with　the　degree　of　blurriness.　The　proposed　method　contains　the　following　steps.　Firstly,　the　LBP　maps　of　an　input　image　are　extracted　with　multiple　radiuses.　And　then,　the　frequency　of　pattern　histogram　is　analyzed　before　part　of　bins　are　chosen　as　the　features.　In　addition,　we　also　take　the　entropy　of　these　bins　as　another　feature.　Finally,　we　learn　the　extracted　features　to　predict　the　image　blurriness　score.　Validation　of　the　proposed　method　is　conducted　on　the　blurred　images　of　LIVE-II,　CSIQ,　TID2008,　TID2013,　LIVE3D　IQA　Phase　I　and　LIVE3D　IQA　Phase　II.　Experimental　results　demonstrate　that　compared　with　the　state-of-the-art　image　quality　assessment　(IQA)　methods,　the　proposed　algorithm　has　notable　advantage　in　correlation　with　subjective　perception　and　computational　complexity.