Depth　image-based　rendering　(DIBR)　techniques　play　an　important　role　in　free　viewpoint　videos　(FVVs),　which　have　a　wide　range　of　applications　including　immersive　entertainment,　remote　monitoring,　education,　etc.　FVVs　are　usually　synthesized　by　DIBR　techniques　in　a　＂blind＂　environment　(without　a　reference　video).　Thus,　an　effective　reference-free　synthesized　video　quality　assessment　(VQA)　metric　is　vital.　At　present,　many　image　quality　assessment　(IQA)　algorithms　for　DIBR-synthesized　images　have　been　proposed,　but　limited　researches　have　been　concerned　about　the　quality　assessment　of　DIBR-synthesized　videos.　To　this　end,　this　paper　proposes　a　novel　reference-free　VQA　method　for　synthesized　videos,　which　operates　in　Spatial　and　Temporal　Domains,　dubbed　as　STD.　The　design　fundamental　of　the　proposed　STD　metric　considers　the　effects　of　two　major　distortions　introduced　by　DIBR　techniques　on　the　visual　quality　of　synthesized　videos.　First,　considering　the　geometric　distortion　introduced　by　DIBR　technologies　can　increase　high-frequency　contents　of　the　synthesized　frame,　the　influence　of　the　geometric　distortion　on　the　visual　quality　of　a　synthesized　video　can　be　effectively　evaluated　by　estimating　high-frequency　energies　of　each　synthesized　frame　in　spatial　domain.　Second,　temporal　inconsistency　caused　by　DIBR　techniques　brings　the　temporal　flicker　distortion,　which　is　one　of　the　most　annoying　artifacts　in　DIBR-synthesized　videos.　In　temporal　domain,　we　quantify　temporal　inconsistency　by　measuring　motion　differences　between　consecutive　frames.　Specifically,　optical　flow　method　is　first　used　to　estimate　the　motion　field　between　adjacent　frames.　Then,　we　calculate　the　structural　similarity　of　adjacent　optical　flow　fields　and　further　adopt　the　structural　similarity　value　to　weight　the　pixel　differences　of　adjacent　optical　flow　fields.　Experiments　show　that　the　above　two　features　are　able　to　well　perceive　the　visual　quality　of　DIBR-synthesized　videos.　Furthermore,　since　the　two　features　are　extracted　from　spatial　and　temporal　domains,　respectively,　we　integrate　them　using　a　linear　weighting　strategy　to　obtain　our　STD　metric,　which　proves　advantageous　over　two　components　and　the　competing　state-of-the-art　I/VQA　methods.　The　source　code　is　available　at　https://github.com/wgc-vsfm/DIBR-video-quality-assessment.