In　this　paper　we　propose　to　blindly　evaluate　the　quality　of　images　synthesized　based　on　a　depth　image-based　rendering　(DIBR)　procedure.　As　an　important　branch　in　virtual　reality　(VR),　superior　DIBR　techniques　provide　free　viewpoints　in　many　real　applications　such　as　remote　surveillance　and　education,　but　few　efforts　have　been　made　to　measure　the　performance　of　DIBR　methods　(i.e.　the　quality　of　DIBR-synthesized　images),　especially　in　the　condition　of　reference　unavailable.　To　this　aim,　we　put　forward　a　new　no-reference　(NR)　image　quality　assessment　(IQA)　model　via　multiscale　analysis,　dubbed　as　MSA.　The　design　philosophy　of　our　proposed　MSA　model　is　that　the　DIBR-introduced　geometry　distortions　damage　the　self-similarity　characteristic　of　natural　images　and　the　damage　degrees　present　regular　variations　at　distinct　scales.　Through　systematically　incorporating　the　measurements　of　the　variations　provided　above,　our　MSA　model　can　faithfully　predict　the　quality　of　images　generated　using　different　DIBR　technologies.　Results　of　experiments　demonstrate　that　the　proposed　blind　MSA　model　has　delivered　noticeably　better　performance　than　state-of-the-art　full-　and　no-reference　IQA　methods.