Camera　images　in　reality　are　easily　affected　by　various　distortions,　such　as　blur,　noise,　blockiness,　and　the　like,　which　damage　the　quality　of　images.　The　complexity　of　distortions　in　camera　images　raises　significant　challenge　for　precisely　predicting　their　perceptual　quality.　In　this　paper,　we　present　an　image　quality　assessment　(IQA)　approach　that　aims　to　solve　this　challenging　problem　to　some　extent.　In　the　proposed　method,　we　first　extract　the　low-level　and　high-level　statistical　features,　which　can　capture　the　quality　degradations　effectively.　On　the　one　hand,　the　first　kind　of　statistical　features　are　extracted　from　the　locally　mean　subtracted　and　contrast　normalized　coefficients,　which　denote　the　low-level　features　in　the　early　human　vision.　On　the　other　hand,　the　recently　proposed　brain　theory　and　neuroscience,　especially　the　free-energy　principle,　reveal　that　the　human　brain　tries　to　explain　its　encountered　visual　scenes　through　an　inner　creative　model,　with　which　the　brain　can　produce　the　projection　for　the　image.　Then,　the　quality　of　perceptions　can　be　reflected　by　the　divergence　between　the　image　and　its　brain　projection.　Based　on　this,　we　extract　the　second　type　of　features　from　the　brain　perception　mechanism,　which　represent　the　high-level　features.　The　low-level　and　high-level　statistical　features　can　play　a　complementary　role　in　quality　prediction.　After　feature　extraction,　we　design　a　neural　network　to　integrate　all　the　features　and　convert　them　to　the　final　quality　score.　Extensive　tests　performed　on　two　real　camera　image　datasets　prove　the　validity　of　our　method　and　its　advantageous　predicting　ability　over　the　competitive　IQA　models.