Free　viewpoint　video　(FVV),　owing　to　its　comprehensive　applications　in　immersive　entertainment,　remote　surveillance　and　distanced　education,　has　received　extensive　attention　and　been　regarded　as　a　new　important　direction　of　video　technology　development.　Depth　image-based　rendering　(DIBR)　technologies　are　employed　to　synthesize　FVV　images　in　the　＂blind＂　environment.　Therefore,　a　real-time　reliable　blind　quality　assessment　metric　is　urgently　required.　However,　existing　stste-of-art　quality　assessment　methods　are　limited　to　estimate　geometric　distortions　generated　by　DIBR.　In　this　research,　a　novel　blind　quality　metric,　measuring　Geometric　Distortions　and　Image　Complexity　(GDIC),　is　proposed　for　DIBR-synthesized　images.　Firstly,　a　DIBR-synthesized　image　is　decomposed　into　wavelet　subbands　by　using　discrete　wavelet　transform.　Then,　we　adopt　canny　operator　to　capture　the　edge　of　wavelet　subbands　and　compute　the　edge　similarity　between　low　-frequency　subband　and　high　frequency　subbands.　The　edge　similarity　is　used　to　quantify　geometric　distortions　in　DIBR-synthesized　images.　Secondly,　a　hybrid　filter　combining　the　autoregressive　and　bilateral　filter　is　adopted　to　compute　image　complexity.　Finally,　the　overall　quality　score　is　calculated　by　normalizing　geometric　distortions　via　image　complexity.　Experiments　show　that　our　proposed　GDIC　is　superior　to　prevailing　image　quality　assessment　metrics,　which　were　intended　for　natural　and　DIBR-synthesized　images.