It　is　known　that　images　available　usually　undergo　some　stages　of　processing　(e.g.,　acquisition,　compression,　transmission　and　display),　and　each　stage　may　introduce　certain　type　of　distortion.　Hence,　images　distorted　by　multiple　types　of　distortions　are　common　in　real　applications.　Research　in　human　visual　perception　has　evidenced　that　the　human　visual　system　(HVS)　is　sensitive　to　image　structural　information.　This　fact　inspires　us　to　design　a　new　blind/no-reference　(NR)　image　quality　assessment　(IQA)　method　to　evaluate　the　visual　quality　of　multiply-distorted　images　based　on　structural　degradation.　Specifically,　quality-aware　features　are　extracted　from　both　the　first-and　high-order　image　structures　by　local　binary　pattern　(LBP)　operators.　Experimental　results　on　two　well-known　multiply-distorted　image　databases　demonstrate　the　outstanding　performance　of　the　proposed　method.