In　the　past　decade,　extensive　image　quality　metrics　have　been　proposed.　The　majority　of　them　are　tailored　for　the　images　that　contain　a　specific　type　of　distortion.　However,　in　practice,　the　images　are　usually　degraded　by　different　types　of　distortions　simultaneously.　This　poses　great　challenges　to　the　existing　quality　metrics.　Motivated　by　this,　this　paper　proposes　a　no-reference　quality　index　for　the　multiply　distorted　images　using　the　biorder　structure　degradation　and　the　nonlocal　statistics.　The　design　philosophy　is　inspired　by　the　fact　that　the　human　visual　system　(HVS)　is　highly　sensitive　to　the　degradations　of　both　the　spatial　contrast　and　the　spatial　distribution,　which　are　prone　to　be　changed　by　the　joint　effects　of　the　multiple　distortions.　Specifically,　the　multiresolution　representation　of　the　image　is　first　built　by　downsampling　to　simulate　the　hierarchical　property　of　the　HVS.　Then,　the　structure　degradation　is　calculated　to　measure　the　spatial　contrast.　Considering　the　fact　that　the　human　visual　cortex　has　the　separate　mechanisms　to　perceive　the　first-　and　second-order　structures,　dubbed　biorder　structures,　the　degradations　of　biorder　structures　are　calculated　to　account　for　the　spatial　contrast,　producing　the　first　group　of　the　quality-aware　features.　Furthermore,　the　nonlocal　self-similarity　statistics　is　calculated　to　measure　the　spatial　distribution,　producing　the　second　group　of　features.　Finally,　all　the　features　are　fed　into　the　random　forest　regression　model　to　learn　the　quality　model　for　the　multiply　distorted　images.　Extensive　experimental　results　conducted　on　the　three　public　databases　demonstrate　the　superiority　of　the　proposed　metric　to　the　state-of-the-art　metrics.　Moreover,　the　proposed　metric　is　also　advantageous　over　the　existing　metrics　in　terms　of　the　generalization　ability.