Opinion-unaware　blind　image　quality　assessment　(OU　BIQA)　refers　to　establishing　a　blind　quality　prediction　model　without　using　the　expensive　subjective　quality　scores,　which　is　a　highly　promising　direction　in　the　BIQA　research.　In　this　article,　we　focus　on　OU　BIQA　and　propose　a　novel　OU　BIQA　method.　Specifically,　in　our　proposed　method,　we　deeply　investigate　the　natural　scene　statistics　(NSS)　and　the　perceptual　characteristics　of　the　human　brain　for　visual　perception.　Accordingly,　a　set　of　quality-aware　NSS　and　perceptual　characteristics-related　features　are　designed　to　characterize　the　image　quality　effectively.　For　inferring　the　image　quality,　we　learn　a　pristine　multivariate　Gaussian　(MVG)　model　on　a　collection　of　pristine　images,　which　serves　as　the　reference　information　for　quality　evaluation.　At　last,　the　quality　of　a　new　given　image　is　defined　by　measuring　the　divergence　between　its　MVG　model　and　the　learned　pristine　MVG　model.　Thorough　experiments　performed　on　seven　popular　image　databases　demonstrate　that　the　proposed　OU　BIQA　method　delivers　superior　performance　to　the　state-of-the-art　OU　BIQA　methods.　The　Matlab　source　code　of　the　proposed　method　will　be　made　publicly　available　at　https://github.com/YT2015?tab=repositories.