Video　quality　assessment　is　critical　in　optimizing　video　coding　techniques.　However,　the　state-of-the-art　methods　have　limited　performance,　which　is　largely　due　to　the　lack　of　large-scale　subjective　databases　for　training.　In　this　work,　a　semi-automatic　labeling　method　is　adopted　to　build　a　large-scale　compressed　video　quality　database,　which　allows　us　to　label　a　large　number　of　compressed　videos　with　manageable　human　workload.　The　resulting　Compressed　Video　quality　database　with　Semi-Automatic　Ratings　(CVSAR),　so　far　the　largest　of　compressed　video　quality　database.　We　train　a　no-reference　compressed　video　quality　assessment　model　with　a　3D　CNN　for　SpatioTemporal　Feature　Extraction　and　Evaluation　(STFEE).　Experimental　results　demonstrate　that　the　proposed　method　outperforms　state-of-the-art　metrics　and　achieves　promising　generalization　performance　in　cross-database　tests.　The　CVSAR　database　has　been　made　publicly　available.　It　can　be　accessed　at　https://github.com/Rocknroll194/CVSAR.　©　1991-2012　IEEE.