Video　compression　leads　to　compression　artifacts,　among　which　Perceivable　Encoding　Artifacts　(PEAs)　degrade　user　perception.　Most　of　existing　state-of-the-art　Video　Compression　Artifact　Removal　(VCAR)　methods　indiscriminately　process　all　artifacts,　thus　leading　to　over-enhancement　in　non-PEA　regions.　Therefore,　accurate　detection　and　location　of　PEAs　is　crucial.　In　this　paper,　we　propose　the　largest-ever　Fine-grained　PEA　database　(FPEA).　First,　we　employ　the　popular　video　codecs,　VVC　and　AVS3,　as　well　as　their　common　test　settings,　to　generate　four　types　of　spatial　PEAs　(blurring,　blocking,　ringing　and　color　bleeding)　and　two　types　of　temporal　PEAs　(flickering　and　floating).　Second,　we　design　a　labeling　platform　and　recruit　sufficient　subjects　to　manually　locate　all　the　above　types　of　PEAs.　Third,　we　propose　a　voting　mechanism　and　feature　matching　to　synthesize　all　subjective　labels　to　obtain　the　final　PEA　labels　with　fine-grained　locations.　Besides,　we　also　provide　Mean　Opinion　Score　(MOS)　values　of　all　compressed　video　sequences.　Experimental　results　show　the　effectiveness　of　FPEA　database　on　both　VCAR　and　compressed　Video　Quality　Assessment　(VQA).　We　envision　that　FPEA　database　will　benefit　the　future　development　of　VCAR,　VQA　and　perception-aware　video　encoders.　The　FPEA　database　has　been　made　publicly　available.　IEEE