This　paper　proposes　to　blindly　evaluate　the　quality　of　images　synthesized　via　a　depth　image-based　rendering　(DIBR)　procedure.　As　a　significant　branch　of　virtual　reality　(VR),　superior　DIBR　techniques　provide　free　viewpoints　in　many　real　applications,　including　remote　surveillance　and　education;　however,　limited　efforts　have　been　made　to　measure　the　performance　of　DIBR　techniques,　or　equivalently　the　quality　of　DIBR-synthesized　views,　especially　in　the　condition　when　references　are　unavailable.　To　achieve　this　aim,　we　develop　a　novel　blind　image　quality　assessment　(IQA)　method　via　multiscale　natural　scene　statistical　analysis　(MNSS).　The　design　principle　of　our　proposed　MNSS　metric　is　based　on　two　new　natural　scene　statistics　(NSS)　models　specific　to　the　DBIR-synthesized　IQA.　First,　the　DIBR-introduced　geometric　distortions　damage　the　local　self-similarity　characteristic　of　natural　images,　and　the　damage　degrees　of　self-similarity　present　particular　variations　at　different　scales.　Systematically　combining　the　measurements　of　the　variations　mentioned　above　can　gauge　the　naturalness　of　the　input　image　and　thus　indirectly　reflect　the　quality　changes　of　images　generated　using　different　DIBR　methods.　Second,　it　was　found　that　the　degradations　in　main　structures　of　natural　images　at　different　scales　remain　almost　the　same,　whereas　the　statistical　regularity　is　destroyed　in　the　DIBR-synthesized　views.　Estimating　the　deviation　of　degradations　in　main　structures　at　different　scales　between　one　DIBR-synthesized　image　and　the　statistical　model,　which　is　constructed　based　on　a　large　number　of　natural　images,　can　quantify　how　a　DIBR　method　damages　the　main　structures　and　thus　infer　the　image　quality.　Via　trials,　the　two　NSS-based　features　extracted　above　can　well　predict　the　quality　of　DIBR-synthesized　images.　Further,　the　two　features　come　from　distinct　points　of　view,　and　we　hence　integrate　them　via　a　straightforward　multiplication　to　derive　the　proposed　blind　MNSS　metric,　which　achieves　better　performance　than　each　component　and　state-of-the-art　quality　methods.