Most　existing　blind　image　quality　assessment　(BIQA)　methods　belong　to　supervised　methods,　which　always　need　a　large　number　of　image　samples　and　expensive　subjective　scores　for　training　a　quality　prediction　model.　In　this　paper,　we　focus　our　attention　on　the　unsupervised　BIQA　methods　and　put　forward　a　novel　unsupervised　approach.　The　main　idea　of　our　method　is　to　quantify　the　image　quality　degradation　through　measuring　the　structure,　naturalness,　and　the　perception　quality　variations　of　the　distorted　image　from　the　pristine　natural　images.　In　specific,　the　structure　variation　is　captured　by　the　deviations　of　the　image　phase　congruency　and　gradients　distributions.　The　naturalness　variation　is　characterized　through　the　distributions　variations　of　the　locally　mean　subtracted　and　contrast　normalized　(MSCN)　coefficients　and　the　products　of　pairs　of　the　adjacent　MSCN　coefficients.　Compared　with　existing　unsupervised　methods,　we　initiatively　introduce　the　perception　quality　measurement　into　the　construction　of　unsupervised　BIQA　method,　which　is　conducted　by　characterizing　the　prediction　discrepancy　between　the　image　and　its　brain　prediction　based　on　the　free-energy　principle　in　the　newly　revealed　brain　theory.　After　feature　extraction,　we　learn　a　pristine　multivariate　Gaussian　(MVG)　model　with　the　extracted　features　from　a　set　of　pristine　natural　images.　The　quality　of　a　new　image　is　finally　defined　as　the　distance　between　its　MVG　model　and　the　learned　pristine　MVG　model.　The　extensive　experiments　conducted　on　LIVE,　TID2013,　CSIQ,　Toyama,　CID2013,　and　the　Waterloo　Exploration　databases　demonstrate　that　the　proposed　method　achieves　comparative　prediction　performance　with　the　state-of-the-art　BIQA　methods.