Recently,　most　dehazed　image　quality　assessment　(DQA)　methods　have　focused　on　estimating　remaining　haze　and　omitting　distortion　impact　from　the　side　effect　of　dehazing　algorithms,　which　leads　to　their　limited　performance.　Addressing　this　problem,　we　propose　a　method　for　learning　both　visibility　and　distortion-aware　features　no-reference　(NR)　dehazed　image　quality　assessment　(VDA-DQA).　Visibility-aware　features　are　exploited　to　characterize　clarity　optimization　after　dehazing,　including　the　brightness-,　contrast-,　and　sharpness-aware　features　extracted　by　the　complex　contourlet　transform　(CCT).　Then,　distortion-aware　features　are　employed　to　measure　the　distortion　artifacts　of　images,　including　the　normalized　histogram　of　the　local　binary　pattern　(LBP)　from　the　reconstructed　dehazed　image　and　the　statistics　of　the　CCT　subbands　corresponding　to　the　chroma　and　saturation　map.　Finally,　all　the　above　features　are　mapped　into　quality　scores　by　support　vector　regression　(SVR).　Extensive　experimental　results　on　six　public　DQA　datasets　verify　the　superiority　of　the　proposed　VDA-DQA　method　in　terms　of　consistency　with　subjective　visual　perception　and　outperform　state-of-the-art　methods.