State　of　the　art　blind　image　quality　assessment　(IQA)　methods　generally　extract　perceptual　features　from　the　training　images,　and　send　them　into　support　vector　machine　(SVM)　to　learn　the　regression　model,　which　could　be　used　to　further　predict　the　quality　scores　of　the　testing　images.　However,　these　methods　need　complicated　training　and　learning,　and　the　evaluation　results　are　sensitive　to　image　contents　and　learning　strategies.　In　this　paper,　two　novel　blind　IQA　metrics　without　training　and　learning　are　firstly　proposed.　The　new　methods　extract　perceptual　features,　i.e.,　the　shape　consistency　of　conditional　histograms,　from　the　joint　histograms　of　neighboring　divisive　normalization　transform　coefficients　of　distorted　images,　and　then　compare　the　length　attribute　of　the　extracted　features　with　that　of　the　reference　images　and　degraded　images　in　the　LIVE　database.　For　the　first　method,　a　cluster　center　is　found　in　the　feature　attribute　space　of　the　natural　reference　images,　and　the　distance　between　the　feature　attribute　of　the　distorted　image　and　the　cluster　center　is　adopted　as　the　quality　label.　The　second　method　utilizes　the　feature　attributes　and　subjective　scores　of　all　the　images　in　the　LIVE　database　to　construct　a　dictionary,　and　the　final　quality　score　is　calculated　by　interpolating　the　subjective　scores　of　nearby　words　in　the　dictionary.　Unlike　the　traditional　SVM　based　blind　IQA　methods,　the　proposed　metrics　have　explicit　expressions,　which　reflect　the　relationships　of　the　perceptual　features　and　the　image　quality　well.　Experiment　results　in　the　publicly　available　databases　such　as　LIVE,　CSIQ　and　TID2008　had　shown　the　effectiveness　of　the　proposed　methods,　and　the　performances　are　fairly　acceptable.