Center-surrounded　receptive　fields,　which　can　be　well　simulated　by　the　Laplacian　of　Gaussian　(LOG)　filter,　have　been　found　in　the　cells　of　the　retina　and　lateral　geniculate　nucleus　(LGN).　With　center-surrounded　receptive　fields,　the　human　visual　system　(HVS)　can　reduce　the　visual　redundancy　by　extracting　the　edges　and　contours　of　objects.　Furthermore,　current　researches　on　image　quality　assessment　(IQA)　have　shown　that　human＇s　perception　of　image　quality　can　be　estimated　by　the　correlation　degree　between　the　extracted　perceptual-aware　features　of　the　reference　and　test　images.　Thus,　this　paper　assesses　the　quality　of　a　video　by　measuring　the　similarity　of　perceptual-aware　features　from　LOG　filtering　between　the　test　video　and　reference　video.　Considering　the　spatial　and　temporal　channel　of　the　human　visual　system　both　include　the　second　derivative　of　Gaussian　function,　we　first　construct　a　three-dimensional　LOG　(3D　LOG)　filter　to　simulate　human　visual　filter　and　to　extract　the　perceptual-aware　features　for　the　design　of　VQA　algorithms.　Moreover,　since　the　correlation　measuring　based　on　2D　LOG　filtering　of　video　spatiotemporal　slice　(STS)　images　can　capture　the　distortion　of　spatiotemporal　motion　structure　accurately　and　effectively,　then　we　apply　the　2D　LOG　filtering　to　video　STS　images　and　using　maximum　pooling　for　distortion　of　vertical　and　horizontal　STS　images　to　improve　prediction　accuracy.　The　performance　of　proposed　algorithms　is　validated　on　the　LIVE　VQA　database.　The　Spearman＇s　rank　correlation　coefficients　of　the　proposed　algorithms　are　all　above　0.82,　which　shows　that　our　methods　are　better　than　that　of　most　mainstream　VQA　methods.　©　2019　SPIE.