High　dynamic　range　(HDR)　content　is　increasingly　entering　our　life　for　super　realistic　representation　of　real-word　scenes.　Compared　to　low　dynamic　(LDR)　images,　HDR　images　contain　significantly　higher　peak　luminance　and　contrast,　making　traditional　LDR　image　quality　assessment　(IQA)　metrics　inefficient　for　HDR　image　applications.　Towards　developing　HDR　IQA,　we　propose　a　new　no-reference　metric　based　on　Retinex　decomposition.　First,　multi-scale　Retinex　decomposition　is　used　to　generate　luminance　and　reflectance　maps　through　gaussian　filtering.　Then,　we　calculate　gradient　similarities　and　natural　scene　statistics　(NSS)　from　the　reflectance　and　luminance　maps,　respectively.　Finally,　all　features　are　fused　by　support　vector　regression　(SVR)　for　score　prediction.　Experimental　results　on　two　databases　demonstrate　the　superiority　of　the　proposed　method　over　the　other　state-of-the-arts　for　HDR　IQA.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.　2024.