Tone　mapping　operators　(TMOs)　are　developed　to　convert　a　high　dynamic　range　(HDR)　image　into　a　low　dynamic　range　(LDR)　one　for　display　with　the　goal　of　preserving　as　much　visual　information　as　possible.　However,　image　quality　degradation　is　inevitable　due　to　the　dynamic　range　compression　during　the　tone-mapping　process.　This　accordingly　raises　an　urgent　demand　for　effective　quality　evaluation　methods　to　select　a　high-quality　tone-mapped　image　(TMI)　from　a　set　of　candidates　generated　by　distinct　TMOs　or　the　same　TMO　with　different　parameter　settings.　A　key　element　to　the　success　of　TMI　quality　evaluation　is　to　extract　effective　features　that　are　highly　consistent　with　human　perception.　Towards　this　end,　this　paper　proposes　a　novel　blind　TMI　quality　metric　by　exploiting　both　local　degradation　characteristics　and　global　statistical　properties　for　feature　extraction.　Several　image　attributes　including　texture,　structure,　colorfulness　and　naturalness　are　considered　either　locally　or　globally.　The　extracted　local　and　global　features　are　aggregated　into　an　overall　quality　via　regression.　Experimental　results　on　two　benchmark　databases　demonstrate　the　superiority　of　the　proposed　metric　over　both　the　state-of-the-art　blind　quality　models　designed　for　synthetically　distorted　images　(SDIs)　and　the　blind　quality　models　specifically　developed　for　TMIs.