Despite　the　acceptable　performance　of　current　full-reference　image　quality　assessment　(IQA)　algorithms,　the　need　for　a　reference　signal　limits　their　application,　and　calls　for　reliable　no-reference　algorithms.　Most　no-reference　IQA　approaches　are　distortion　specific,　aiming　to　measure　image　blur,　JPEG　blocking　or　JPEG2000　ringing　artifacts　respectively.　In　this　paper,　we　proposed　a　no-reference　IQA　algorithm　based　on　the　statistic　property　of　principal　component　analysis　on　nature　image,　named　SPCA,　which　does　not　assume　any　specific　type　of　distortion　of　the　image.　The　method　gets　statistics　of　discrete　cosine　transform　coefficients　from　the　distort　image＇s　principal　components.　Those　features　are　trained　by　nu-support　vector　regression　method　and　finally　test　on　LIVE　database.　The　experimental　results　show　a　high　correlation　with　human　perception　of　quality　(averagely　over　90%　by　scores　of　SROCC),　which　is　fairly　competitive　with　the　existing　no-reference　IQA　metrics.