The　most　widely　used　video　encoders　share　a　common　hybrid　coding　framework　that　includes　block-based　motion　estimation/compensation　and　block-based　transform　coding.　Despite　their　high　coding　efficiency,　the　encoded　videos　often　exhibit　visually　annoying　artifacts,　denoted　as　Perceivable　Encoding　Artifacts　(PEAs),　which　significantly　degrade　the　visual　Quality-of-Experience　(QoE)　of　end　users.　To　monitor　and　improve　visual　QoE,　it　is　crucial　to　develop　subjective　and　objective　measures　that　can　identify　and　quantify　various　types　of　PEAs.　In　this　work,　we　make　the　first　attempt　to　build　a　large-scale　subject-labeled　database　composed　of　H.265/HEVC　compressed　videos　containing　various　PEAs.　The　database,　namely　the　PEA265,　includes　4　types　of　spatial　PEAs　(i.e.　blurring,　blocking,　ringing　and　color　bleeding)　and　2　types　of　temporal　PEAs　(i.e.　flickering　and　floating).　Each　containing　at　least　60,000　image　or　video　patches　with　positive　and　negative　labels.　Based　on　the　PEA265　database,　we　develop　and　optimize　Convolutional　Neural　Networks　(CNNs)　to　objectively　recognize　different　types　of　PEAs.　Experiments　show　that　our　architecture　is　capable　of　identifying　the　6　types　of　PEAs　with　an　accuracy　over　86%.　To　further　demonstrate　its　application,　we　explore　the　relationship　between　collected　PEA　intensities　and　subjective　quality　scores　of　compressed　videos.　A　quality　metric　is　consequently　proposed　with　superior　performance　in　terms　of　correlation　to　Mean　Opinion　Score　(MOS)　values.　We　believe　that　the　PEA265　database　and　our　findings　will　benefit　the　future　development　of　video　quality　assessment　methods　and　perceptually　motivated　video　encoders.