Currently,　many　tone　mapping　operators　(TMOs)　have　been　provided　to　compress　high　dynamic　range　images　to　low　dynamic　range　(LDR)　images　for　visualizing　them　on　the　common　displays.　Since　quality　degradation　is　inevitably　induced　by　compression,　how　to　evaluate　the　obtained　LDR　images　is　indeed　a　headache　problem.　Until　now,　only　a　few　full　reference　(FR)　image　quality　assessment　metrics　have　been　proposed.　However,　they　highly　depend　on　reference　image　and　neglect　human　visual　system　characteristics,　hindering　the　practical　applications.　In　this　paper,　we　propose　an　effective　blind　quality　assessment　method　of　tone-mapped　image　without　access　to　reference　image.　Inspired　by　that　the　performance　of　existing　TMOs　largely　depend　on　the　brightness　and　chromatic　and　structural　properties　of　a　scene,　we　evaluate　the　perceptual　quality　from　the　perspective　of　color　information　processing　in　the　brain.　Specifically,　motivated　by　the　physiological　and　psychological　evidence,　we　simulate　the　responses　of　single-opponent　(SO)　and　double-opponent　(DO)　cells,　which　play　an　important　role　in　the　processing　of　the　color　information.　To　represent　the　textural　information,　we　extract　three　features　from　gray-level　co-occurrence　matrix　(GLCM)　calculated　from　SO　responses.　Meanwhile,　both　GLCM　and　local　binary　pattern　descriptor　are　employed　to　extract　texture　and　structure　in　the　responses　of　DO　cells.　All　these　extracted　features　and　associated　subjective　ratings　are　learned　to　reveal　the　connection　between　feature　space　and　human　opinion　score.　Extensive　experiments　demonstrate　that　the　proposed　method　outperforms　the　state-of-the-art　blind　quality　assessment　methods　and　is　comparable　with　the　popular　FR　methods　on　two　recently　published　tone-mapped　image　databases.　©　1982-2012　IEEE.