Video　coding　technology　makes　the　required　storage　and　transmission　bandwidth　of　video　services　decrease　by　reducing　the　bitrate　of　the　video　stream.　However,　the　compressed　video　signals　may　involve　perceivable　information　loss,　especially　when　the　video　is　overcompressed.　In　such　cases,　the　viewers　can　observe　visually　annoying　artifacts,　namely,　Perceivable　Encoding　Artifacts　(PEAs),　which　degrade　their　perceived　video　quality.　To　monitor　and　measure　these　PEAs　(including　blurring,　blocking,　ringing　and　color　bleeding),　we　propose　an　objective　video　quality　metric　named　Saliency-Aware　Artifact　Measurement　(SAAM)　without　any　reference　information.　The　SAAM　metric　first　introduces　video　saliency　detection　to　extract　interested　regions　and　further　splits　these　regions　into　a　finite　number　of　image　patches.　For　each　image　patch,　the　data-driven　model　is　utilized　to　evaluate　intensities　of　PEAs.　Finally,　these　intensities　are　fused　into　an　overall　metric　using　Support　Vector　Regression　(SVR).　In　experiment　section,　we　compared　the　SAAM　metric　with　other　popular　video　quality　metrics　on　four　publicly　available　databases:　LIVE,　CSIQ,　IVP　and　FERIT-RTRK.　The　results　reveal　the　promising　quality　prediction　performance　of　the　SAAM　metric,　which　is　superior　to　most　of　the　popular　compressed　video　quality　evaluation　models.