Compressed　videos　often　exhibit　visually　annoying　artifacts,　known　as　Perceivable　Encoding　Artifacts　(PEAs),　which　dramatically　degrade　video　visual　quality.　Subjective　and　objective　measures　capable　of　identifying　and　quantifying　various　types　of　PEAs　are　critical　in　improving　visual　quality.　In　this　letter,　we　investigate　the　influence　of　four　spatial　PEAs　(i.e.　blurring,　blocking,　bleeding,　and　ringing)　and　two　temporal　PEAs　(i.e.　flickering　and　floating)　on　video　quality.　For　spatial　artifacts,　we　propose　a　visual　saliency　model　with　a　low　computational　cost　and　higher　consistency　with　human　visual　perception.　In　terms　of　temporal　artifacts,　self-attention　based　TimeSFormer　is　improved　to　detect　temporal　artifacts.　Based　on　the　six　types　of　PEAs,　a　quality　metric　called　Saliency-Aware　Spatio-Temporal　Artifacts　Measurement　(SSTAM)　is　proposed.　Experimental　results　demonstrate　that　the　proposed　method　outperforms　state-of-the-art　metrics.　We　believe　that　SSTAM　will　be　beneficial　for　optimizing　video　coding　techniques.