Free　viewpoint　video　(FVV)　has　received　considerable　attention　owing　to　its　widespread　applications　in　several　areas　such　as　immersive　entertainment,　remote　surveillance　and　distanced　education.　Since　FVV　images　are　synthesized　via　a　depth　image-based　rendering　(DIBR)　procedure　in　the　blind　environment　(without　reference　images),　a　real-time　and　reliable　blind　quality　assessment　metric　is　urgently　required.　However,　the　existing　image　quality　assessment　metrics　are　insensitive　to　the　geometric　distortions　engendered　by　DIBR.　In　this　research,　a　novel　blind　method　of　DIBR-synthesized　images　is　proposed　based　on　measuring　geometric　distortion,　global　sharpness　and　image　complexity.　First,　a　DIBR-synthesized　image　is　decomposed　into　wavelet　subbands　by　using　discrete　wavelet　transform.　Then,　the　Canny　operator　is　employed　to　detect　the　edges　of　the　binarized　low-frequency　subband　and　high-frequency　subbands.　The　edge　similarities　between　the　binarized　low-frequency　subband　and　high-frequency　subbands　are　further　computed　to　quantify　geometric　distortions　in　DIBR-synthesized　images.　Second,　the　log-energies　of　wavelet　subbands　are　calculated　to　evaluate　global　sharpness　in　DIBR-synthesized　images.　Third,　a　hybrid　filter　combining　the　autoregressive　and　bilateral　filters　is　adopted　to　compute　image　complexity.　Finally,　the　overall　quality　score　is　derived　to　normalize　geometric　distortion　and　global　sharpness　by　the　image　complexity.　Experiments　show　that　our　proposed　quality　method　is　superior　to　the　competing　reference-free　state-of-the-art　DIBR-synthesized　image　quality　models.