With　the　increasing　prevalence　of　free-viewpoint　video　applications,　virtual　view　synthesis　has　attracted　extensive　attention.　In　view　synthesis,　a　new　viewpoint　is　generated　from　the　input　color　and　depth　images　with　a　depth-image-based　rendering　(DIBR)　algorithm.　Current　quality　evaluation　models　for　view　synthesis　typically　operate　on　the　synthesized　images,　i.e.　after　the　DIBR　process,　which　is　computationally　expensive.　So　a　natural　question　is　that　can　we　infer　the　quality　of　DIBR-based　synthesized　images　using　the　input　color　and　depth　images　directly　without　performing　the　intricate　DIBR　operation.　With　this　motivation,　this　paper　presents　a　no-reference　image　quality　prediction　model　for　view　synthesis　via　COlor-Depth　Image　Fusion,　dubbed　CODIF,　where　the　actual　DIBR　is　not　needed.　First,　object　boundary　regions　are　detected　from　the　color　image,　and　a　Wavelet-based　image　fusion　method　is　proposed　to　imitate　the　interaction　between　color　and　depth　images　during　the　DIBR　process.　Then　statistical　features　of　the　interactional　regions　and　natural　regions　are　extracted　from　the　fused　color-depth　image　to　portray　the　influences　of　distortions　in　color/depth　images　on　the　quality　of　synthesized　views.　Finally,　all　statistical　features　are　utilized　to　learn　the　quality　prediction　model　for　view　synthesis.　Extensive　experiments　on　public　view　synthesis　databases　demonstrate　the　advantages　of　the　proposed　metric　in　predicting　the　quality　of　view　synthesis,　and　it　even　suppresses　the　state-of-the-art　post-DIBR　view　synthesis　quality　metrics.