Color　correction　plays　an　important　role　in　the　image　processing　field.　But　substantial　research　on　the　assessment　of　color　correction　is　still　insufficient.　In　this　paper,　we　present　an　image　quality　assessment　metric　for　color　correction.　It　assesses　the　color　consistency　between　the　reference　and　target/result　images　of　color　correction　according　to　their　color　contrast　similarity　and　color　value　difference.　Both　the　average　difference　and　difference　span　are　considered　during　the　assessment.　To　compensate　for　the　scene　difference　between　the　reference　and　target/result　images　of　color　correction,　we　propose　to　use　an　image　registration　algorithm　to　build　their　matching　relationship,　upon　which　a　matching　image　is　built.　The　matching　image　has　the　same　scene　as　the　target　image　and　the　same　color　feature　as　the　reference　image,　and　thus　the　matching　image　is　regarded　as　the　real　reference　image　of　our　color　correction　assessment.　Furthermore,　we　combine　a　confidence　map　of　the　matching　image　and　a　saliency　map　of　the　target/result　image　as　a　weighting　map　for　assessment,　which　helps　to　improve　the　consistency　between　the　objective　and　subjective　assessment　results.　The　experimental　results　show　that　our　color　correction　assessment　metric　has　better　correlation,　accuracy,　and　monotonicity　with　users＇　subjective　scores　than　19　state-of-the-art　metrics.