Depth-image-based　rendering　(DIBR)　is　a　fundamental　technique　in　free　viewpoint　video,　which　is　widely　adopted　to　synthesize　virtual　viewpoints.　The　warping　and　rendering　operations　in　DIBR　generally　introduce　geometric　distortions　and　sharpness　change.　The　state-of-the-art　quality　indices　are　limited　in　dealing　with　such　images　since　they　are　sensitive　to　geometric　changes.　In　this　paper,　a　new　quality　model　for　DIBR-synthesized　view　images　is　presented　by　measuring　LOcal　Geometric　distortions　in　disoccluded　regions　and　global　Sharpness　(LOGS).　A　disoccluded　region　detection　method　is　first　proposed　using　SIFT-flow-based　warping.　Then,　the　sizes　and　distortion　strength　of　local　disoccluded　regions　are　combined　to　generate　a　score.　Furthermore,　a　reblurring-based　strategy　is　proposed　to　quantify　the　global　sharpness.　Finally,　the　overall　quality　score　is　calculated　by　pooling　the　scores　of　local　disoccluded　regions　and　global　sharpness.　Experiments　on　four　public　DIBR-synthesized　image/video　databases　show　the　superiority　of　the　proposed　metric　over　the　state-of-the-art　quality　models.　The　proposed　method　is　further　adopted　for　boosting　the　performances　of　existing　quality　metrics　and　benchmarking　DIBR　algorithms,　both　achieving　very　promising　results.