In　this　work,　we　resolve　a　big　challenge　that　most　current　image　quality　metrics　(IQMs)　are　unavailable　across　different　image　contents,　especially　simultaneously　coping　with　natural　scene　(NS)　images　or　screen　content　(SC)　images.　By　comparison　with　existing　works,　this　paper　deploys　on-line　and　off-line　data　for　proposing　a　unified　no-reference　(NR)　IQM,　not　only　applied　to　different　distortion　types　and　intensities　but　also　to　various　image　contents　including　classical　NS　images　and　prevailing　SC　images.　Our　proposed　NR　IQM　is　developed　with　two　data-driven　learning　processes　following　feature　extraction,　which　is　based　on　scene　statistic　models,　free-energy　brain　principle,　and　human　visual　system　(HVS)　characteristics.　In　the　first　process,　the　scene　statistic　models　and　an　image　retrieve　technique　are　combined,　based　on　on-line　and　off-line　training　instances,　to　derive　a　novel　loose　classifier　for　retrieving　clean　images　and　helping　to　infer　the　image　content.　In　the　second　process,　the　features　extracted　by　incorporating　the　inferred　image　content,　free-energy　and　low-level　perceptual　characteristics　of　the　HVS　are　learned　by　utilizing　off-line　training　samples　to　analyze　the　distortion　types　and　intensities　and　thereby　to　predict　the　image　quality.　The　two　processes　mentioned　above　depend　on　a　gigantic　quantity　of　training　data,　much　exceeding　the　number　of　images　applied　to　performance　validation,　and　thus　make　our　model＇s　performance　more　reliable.　Through　extensive　experiments,　it　has　been　validated　that　the　proposed　blind　IQM　is　capable　of　simultaneously　inferring　the　quality　of　NS　and　SC　images,　and　it　has　attained　superior　performance　as　compared　with　popular　and　state-of-the-art　IQMs　on　the　subjective　NS　and　SC　image　quality　databases.　The　source　code　of　our　model　will　be　released　with　the　publication　of　the　paper　at　https://kegu.netlify.com.