New　challenges　have　been　brought　out　along　with　the　emerging　of　3D-related　technologies,　such　as　virtual　reality,　augmented　reality　(AR),　and　mixed　reality.　Free　viewpoint　video　(FVV),　due　to　its　applications　in　remote　surveillance,　remote　education,　and　so　on,　based　on　the　flexible　selection　of　direction　and　viewpoint,　has　been　perceived　as　the　development　direction　of　next-generation　video　technologies　and　has　drawn　a　wide　range　of　researchers＇　attention.　Since　FVV　images　are　synthesized　via　a　depth　image-based　rendering　(DIBR)　procedure　in　the　＂blind＂　environment　(without　reference　images),　a　reliable　real-time　blind　quality　evaluation　and　monitoring　system　is　urgently　required.　But　existing　assessment　metrics　do　not　render　human　judgments　faithfully　mainly　because　geometric　distortions　are　generated　by　DIBR.　To　this　end,　this　paper　proposes　a　novel　referenceless　quality　metric　of　DIBR-synthesized　images　using　the　autoregression　(AR)-based　local　image　description.　It　was　found　that,　after　the　AR　prediction,　the　reconstructed　error　between　a　DIBR-synthesized　image　and　its　AR-predicted　image　can　accurately　capture　the　geometry　distortion.　The　visual　saliency　is　then　leveraged　to　modify　the　proposed　blind　quality　metric　to　a　sizable　margin.　Experiments　validate　the　superiority　of　our　no-reference　quality　method　as　compared　with　prevailing　full-,　reduced-,　and　no-reference　models.