Perceptual　image　quality　assessment　(IQA)　plays　an　important　role　in　numerous　applications,　including　image　restoration,　compression,　enhancement,　and　others.　Although　many　works　have　been　conducted　on　individually　distorted　IQA　problems　and　have　achieved　encouraging　results,　few　studies　have　been　conducted　on　multiple　distorted　(MD)　IQA　problems.　Thus,　limited　progress　has　been　made.　In　this　paper,　we　propose　a　novel　no　reference　image　quality　assessment　(NR-IQA)　method,　named　improved　multiscale　local　binary　pattern　(IMLBP),　for　addressing　multiply　distorted　IQA　problems.　The　image　structures　are　sensitive　to　image　distortions,　which　motivates　us　to　utilize　the　structural　characteristics　for　overall　image　quality　prediction.　We　improved　the　local　binary　pattern　(LBP)　by　considering　the　human　visual　mechanism　to　better　extract　the　structural　information.　The　IMLBP　contains　two　parts,　the　LBP　and　the　radius　difference　LBP　(DLBP).　The　DLBP　reflects　the　values＇　changes　in　the　radial　direction.　Specifically,　when　the　radius　value　is　small,　the　proposed　descriptor　is　computed　to　represent　microstructural　information.　Conversely,　it　represents　macrostructural　information　when　the　radius　becomes　large.　Moreover,　to　better　mimick　the　human　visual　mechanism,　the　IMLBP　is　computed　with　the　multiscale　strategy　and　the　operation　is　based　on　a　patch　unit　whose　size　is　proportional　to　the　radius　value.　The　frequency　histogram　of　feature　maps　is　transformed　to　feature　vectors.　Subsequently,　a　predictable　function　trained　by　the　support　vector　regression　is　used　to　infer　the　overall　quality　score.　Experimental　results　show　that　the　proposed　method　outperforms　most　state-of-the-art　IQA　metrics　on　publicly　available　multiply　distorted　image　databases.