With　multitudes　of　image　processing　applications,　image　quality　assessment　(IQA)　has　become　a　pre-requisite　for　obtaining　maximally　distinctive　statistics　from　images.　Despite　the　widespread　research　in　this　domain　over　several　years,　existing　IQA　algorithms　have　a　number　of　key　limitations　concerning　different　image　distortion　types　and　algorithms＇　computational　efficiency.　Images　that　are　synthesized　using　depth　image-based　rendering　have　applications　in　various　disciplines,　such　as　free　view-point　videos,　which　enable　synthesis　of　novel　realistic　images　in　the　referenceless　environment.　In　the　literature,　very　few　no-reference　(NR)　quality　assessment　metrics　of　three-dimensional　(3-D)　synthesized　images　are　proposed,　and　most　of　them　are　computationally　expensive,　which　makes　it　difficult　for　them　to　be　deployed　in　real-time　applications.　In　this　paper,　we　attribute　the　geometrically　distorted　pixels　as　outliers　in　3-D　synthesized　images.　This　assumption　is　validated　using　the　three　sigma　rule-based　robust　outlyingness　ratio.　We　propose　a　novel　fast　and　accurate　blind　IQA　metric　of　3-D　synthesized　images　using　nonlinear　median　filtering　since　the　median　filtering　has　the　capability　of　identifying　and　removing　outliers.　The　advantages　of　the　proposed　algorithm　are　twofold.　First,　it　uses　a　simple　technique,　i.　e.,　median　filtering,　to　capture　the　level　of　geometric　and　structural　distortions　(up　to　some　extend).　Second,　the　proposed　algorithm　has　higher　computational　efficiency.　Experiments　show　the　superiority　of　the　proposed　NR　IQA　algorithm　over　existing　state-of-the-art　full-,　reduced-,　and　NR　IQA　methods,　in　terms　of　both　predicting　accuracy　and　computational　complexity.