Recently,　most　dehazed　image　quality　assessment　(DQA)　methods　mainly　focus　on　the　estimation　of　remaining　haze,　omitting　the　impact　of　distortions　from　the　side　effect　of　dehazing　algorithms,　which　lead　to　their　limited　performance.　Addressing　this　problem,　we　proposed　a　learning　both　Visibility　and　Distortion　Aware　features　no-reference　(NR)　Dehazed　image　Quality　Assessment　method　(VDA-DQA).　Visibility　aware　features　are　exploited　to　characterize　clarity　optimization　after　dehazing,　including　the　brightness,　contrast,　and　sharpness　aware　feature　extracted　by　complex　contourlet　transform　(CCT).　Then,　distortion　aware　features　are　employed　to　measure　the　distortion　artifacts　of　images,　including　the　normalized　histogram　of　local　binary　pattern　(LBP)　from　the　reconstructed　dehazed　image　and　the　statistics　of　the　CCT　sub-bands　corresponding　to　chroma　and　saturation　map.　Finally,　all　the　above　features　are　mapped　into　the　quality　scores　by　the　support　vector　regression　(SVR).　Extensive　experimental　results　on　six　public　DQA　datasets　verify　the　superiority　of　proposed　VDA-DQA　in　terms　of　the　consistency　with　subjective　visual　perception,　and　outperforms　the　state-of-the-art　methods.The　source　code　of　VDA-DQA　is　available　at　https://github.com/li181119/VDA-DQA.　IEEE