The　booming　of　High　Definition　(HD)　and　Ultra-HD　(UHD)　videos　in　the　last　decade　has　greatly　promoted　the　use　of　video　display　on　diversified　terminals　such　as　TV,　tablets　and　smartphones.　To　present　more　video　contents　with　high　quality　to　users,　it　is　imperative　to　assess　the　visual　quality　of　videos.　The　approach　denoted　as　Video　Quality　Assessment　(VQA)　has　become　an　appealing　problem　to　researchers.　In　this　work,　we　take　the　video　contents　into　consideration　besides　of　media　format　and　pixel　quality.　This　paper　utilizes　four　measures,　picture　resolution,　bitrates,　Spatial　Information　(SI)　and　Temporal　Information　(TI),　to　represent　the　visual　quality　in　four　separate　dimensions.　Then,　a　subjective　database　is　constructed　to　train　a　neural　network　that　is　capable　of　scaling　content-aware　video　qualities.　Experiments　have　shown　that　our　method　is　superior　to　human　observers　in　terms　of　correlations　to　Mean　Opinion　Score　(MOS)　values.