Existing　no-reference　(NR)　image　quality　assessment　(IQA)　metrics　are　still　not　convincing　for　evaluating　the　quality　of　the　camera-captured　images.　Toward　tackling　this　issue,　we,　in　this　article,　establish　a　novel　NR　quality　metric　for　quantifying　the　quality　of　the　camera-captured　images　reliably.　Since　the　image　quality　is　hierarchically　perceived　from　the　low-level　preliminary　visual　perception　to　the　high-level　semantic　comprehension　in　the　human　brain,　in　our　proposed　metric,　we　characterize　the　image　quality　by　exploiting　both　the　low-level　image　properties　and　the　high-level　semantics　of　the　image.　Specifically,　we　extract　a　series　of　low-level　features　to　characterize　the　fundamental　image　properties,　including　the　brightness,　saturation,　contrast,　noiseness,　sharpness,　and　naturalness,　which　are　highly　indicative　of　the　camera-captured　image　quality.　Correspondingly,　the　high-level　features　are　designed　to　characterize　the　semantics　of　the　image.　The　low-level　and　high-level　perceptual　features　play　complementary　roles　in　measuring　the　image　quality.　To　infer　the　image　quality,　we　employ　the　support　vector　regression　(SVR)　to　map　all　the　informative　features　to　a　single　quality　score.　Thorough　tests　conducted　on　two　standard　camera-captured　image　databases　demonstrate　the　effectiveness　of　the　proposed　quality　metric　in　assessing　the　image　quality　and　its　superiority　over　the　state-of-the-art　NR　quality　metrics.　The　source　code　of　the　proposed　metric　for　camera-captured　images　is　released　at　https://github.com/YT2015?tab=repositories.