As　an　extension　of　Discrete　and　Complex　Wavelet　Transform,　Quaternion　Wavelet　Transform　(QWT)　has　attracted　extensive　attention　in　the　past　few　years,　because　it　can　provide　better　analytic　representation　for　2D　images.　The　QWT　of　an　image　consists　of　four　parts,　i.e.,　one　magnitude　part　and　three　phase　parts.　The　magnitude　is　nearly　shift-invariant,　which　characterizes　features　at　any　spatial　location,　and　the　three　phases　represent　the　structure　of　these　features.　This　indicates　that　QWT　is　more　powerful　in　representing　image　structures,　and　thus　is　suitable　for　image　quality　evaluation.　In　this　paper,　an　efficient　and　effective　Camera　Image　Quality　Metric　(CIQM)　is　proposed　based　on　QWT,　which　is　utilized　to　describe　the　intrinsic　structures　of　an　image.　For　an　image,　it　is　first　decomposed　by　QWT　with　three　scales.　Then,　for　each　scale,　the　magnitude　and　entropy　of　the　subband　coefficients,　and　natural　scene　statistics　of　the　third　phase　are　calculated.　The　magnitude　is　utilized　to　describe　the　generalized　spectral　behavior,　and　the　entropy　is　used　to　encode　die　generalized　information　of　distortions.　Since　the　third　phase　of　QWT　is　considered　to　be　texture　feature,　the　natural　scene　statistics　of　the　third　phase　of　QWT　is　used　to　measure　structure　degradations　in　the　proposed　method.　All　these　features　reflect　the　self-similarity　and　independency　of　image　content,　which　can　effectively　reflect　image　distortions.　Finally,　random　forest　is　utilized　to　build　the　quality　model.　Experiments　conducted　on　three　camera　image　databases　and　two　multiply　distorted　image　databases　have　proved　that　CIQM　outperforms　the　relevant　state-of-the-art　models　for　both　authentically　distorted　images　and　multiply　distorted　images.