Digital　images　in　the　real　world　are　created　by　a　variety　of　means　and　have　diverse　properties.　A　photographical　natural　scene　image　(NSI)　may　exhibit　substantially　different　characteristics　from　a　computer　graphic　image　(CGI)　or　a　screen　content　image　(SCI).　This　casts　major　challenges　to　objective　image　quality　assessment,　for　which　existing　approaches　lack　effective　mechanisms　to　capture　such　content　type　variations,　and　thus　are　difficult　to　generalize　from　one　type　to　another.　To　tackle　this　problem,　we　first　construct　a　cross-content-type　(CCT)　database,　which　contains　1,320　distorted　NSIs,　CGIs,　and　SCIs,　compressed　using　the　high　efficiency　video　coding　(HEVC)　intra　coding　method　and　the　screen　content　compression　(SCC)　extension　of　HEVC.　We　then　carry　out　a　subjective　experiment　on　the　database　in　a　well-controlled　laboratory　environment.　Moreover,　we　propose　a　unified　content-type　adaptive　(UCA)　blind　image　quality　assessment　model　that　is　applicable　across　content　types.　A　key　step　in　UCA　is　to　incorporate　the　variations　of　human　perceptual　characteristics　in　viewing　different　content　types　through　a　multi-scale　weighting　framework.　This　leads　to　superior　performance　on　the　constructed　CCT　database.　UCA　is　training-free,　implying　strong　generalizability.　To　verify　this,　we　test　UCA　on　other　databases　containing　JPEG,　MPEG-2,　H.264,　and　HEVC　compressed　images/videos,　and　observe　that　it　consistently　achieves　competitive　performance.