Most　learning-based　no-reference　(NR)　video　quality　assessment　(VQA)　needs　to　be　trained　with　a　lot　of　subjective　quality　scores.　However,　it　is　currently　difficult　to　obtain　a　large　volume　of　subjective　scores　for　videos.　Inspired　by　the　success　of　full-reference　VQA　methods　based　on　the　spatiotemporal　slice　(STS)　images　in　the　extraction　of　perceptual　features　and　evaluation　of　video　quality,　this　paper　adopts　multi-directional　video　STS　images,　which　are　images　composed　of　multi-directional　sections　of　video　data,　to　deal　with　the　lacking　of　subjective　quality　scores.　By　sampling　the　STS　images　of　video　into　image　patches　and　adding　noise　to　the　quality　labels　of　patches,　a　successful　NR　VQA　model　based　on　multi-directional　STS　images　and　neural　network　training　is　proposed.　Specifically,　first,　we　select　the　subjective　database　that　currently　contains　the　largest　number　of　real　distortion　videos　as　the　test　set.　Second,　we　perform　multi-directional　STS　extraction　on　the　videos　and　sample　the　local　patches　from　the　multi-directional　STS　to　augment　the　training　sample　set.　Besides,　we　add　some　noise　to　the　quality　label　of　the　local　patches.　Third,　a　reasonable　deep　neural　network　is　constructed　and　trained　to　obtain　a　local　quality　prediction　model　for　each　patch　in　the　STS　image,　and　then　the　quality　of　an　entire　video　is　obtained　by　averaging　the　model　prediction　results　of　multi-directional　STS　images.　Finally,　the　experiment　results　indicate　that　the　proposed　method　tackles　the　insufficiency　of　training　samples　in　small　subjective　VQA　dataset　and　obtains　a　high　correlation　with　the　subjective　evaluation.　©　2019　SPIE.