Blind/no-reference　image　quality　assessment　is　performed　to　accurately　evaluate　the　perceptual　quality　of　a　distorted　image　without　prior　information　from　a　reference　image.　In　this　paper,　an　effective　blind　image　quality　assessment　approach　based　on　entropy　differences　in　the　discrete　cosine　transform　domain　for　natural　images　is　proposed.　Information　entropy　is　an　effective　measure　of　the　amount　of　information　in　an　image.　We　find　the　discrete　cosine　transform　coefficient　distribution　of　distorted　natural　images　shows　a　pulse-shape　phenomenon,　which　directly　affects　the　differences　of　entropy.　Then,　a　Weibull　model　is　used　to　fit　the　distributions　of　natural　and　distorted　images.　This　is　because　the　Weibull　model　sufficiently　approximates　the　pulse-shape　phenomenon　as　well　as　the　sharp-peak　and　heavy-tail　phenomena　of　natural　scene　statistics　rules.　Four　features　that　are　related　to　entropy　differences　and　human　visual　system　are　extracted　from　the　Weibull　model　for　three　scaling　images.　Image　quality　is　assessed　by　the　support　vector　regression　method　based　on　the　extracted　features.　This　blind　Weibull　statistics　algorithm　is　thoroughly　evaluated　using　three　widely　used　databases:　LIVE,　TID2008,　and　CSIQ.　The　experimental　results　show　that　the　performance　of　the　proposed　blind　Weibull　statistics　method　is　highly　consistent　with　that　of　human　visual　perception　and　greater　than　that　of　the　state-of-the-art　blind　and　full-reference　image　quality　assessment　methods　in　most　cases.