No-reference　image　quality　assessment　(NR　IQA)　has　wide　applicability　to　many　problems.　This　paper　focuses　on　the　mechanism　of　divisive　normalization　transform　(DNT)　which　simulates　the　behavior　of　visual　cortex　neurons　to　extract　the　independent　components　of　natural　images,　analyzes　the　difference　between　the　statistics　of　neighboring　DNT　coefficients　of　the　images　of　a　variety　of　distortion,　and　proposes　a　novel　solution　for　NR　IQA　metric　design.　We　demonstrate　that　measuring　the　statistical　independence　between　neighboring　DNT　coefficients　could　provide　features　useful　for　quality　assessment.　The　performance　of　the　proposed　method　is　quite　satisfactory　when　it　was　tested　on　the　popular　LIVE,　CSIQ　and　TID2008　databases.　The　experimental　results　are　fairly　competitive　with　the　existing　NR　IQA　metrics.