In　this　article,　we　propose　an　efficient　joint　image　quality　assessment　and　enhancement　algorithm　for　the　3-D-synthesized　images　using　a　global　predictor,　namely,　kernel　ridge　regression　(KRR).　Recently,　a　few　prediction-based　image　quality　assessment　(IQA)　algorithms　have　been　proposed　for　3-D-synthesized　images.　These　algorithms　use　efficient　prediction　algorithms　and　try　to　predict　all　the　regions　efficiently,　except　the　boundaries　of　the　regions　with　geometric　distortions.　Unfortunately,　these　algorithms　only　count　the　number　of　pixels　along　the　boundaries　of　the　regions　with　geometric　distortions　and　subsequently,　calculate　the　quality　scores.　With　this　view,　we　propose　a　new　algorithm　for　3-D-synthesized　images　based　upon　the　global　KRR-based　predictor,　which　estimates　the　complete　distortion　surface　with　geometric　distortions.　Further,　it　uses　the　distortion　surface　to　estimate　the　perceptual　quality　of　the　3-D-synthesized　images.　Also,　the　joint　quality　assessment　and　enhancement　algorithms　for　3-D-synthesized　images　are　missing　in　literature.　With　this　view,　we　propose　to　estimate　the　distortion　map　of　the　geometric　distortions　via　the　same　predictor　used　in　quality　estimation　and　it　subsequently　enhances　the　perceptual　quality　of　the　3-D-synthesized　images.　The　performance　of　the　proposed　quality　assessment　algorithm　is　better　than　the　existing　IQA　algorithms.　Also,　the　proposed　quality　enhancement　algorithm　is　promising,　significantly　enhancing　the　perceptual　quality　of　3-D-synthesized　images.　©　1982-2012　IEEE.