Image　Quality　Assessment　(IQA)　is　one　of　the　fundamental　problems　in　the　fields　of　image　processing,　image/　video　coding　and　transmission,　and　so　on.　In　this　paper,　a　Blind　Image　Quality　Assessment　(BIQA)　approach　with　channel　attention　based　deep　Residual　Network　(ResNet)and　extended　LargeVis　dimensionality　reduction　is　proposed.　Firstly,　ResNet50　with　channel　attention　mechanism　is　used　as　the　backbone　network　to　extract　the　deep　features　from　the　image.　In　order　to　reduce　the　dimensionality　of　the　deep　features,　LargeVis,　which　is　originally　designed　for　the　visualization　of　large　scale　high-dimensional　data,　is　extended　by　using　Support　Vector　Regression　(SVR)　to　perform　on　a　single　feature　vector　data.　The　extended　LargeVis　can　remove　the　redundant　information　of　the　deep　features　so　as　to　obtain　a　low-dimensional　and　discriminative　feature　rep-resentation.　Finally,　the　quality　prediction　model　is　established　by　using　SVR　as　the　fitting　method.　The　low-dimensional　feature　representation　and　quality　score　of　the　image　form　the　pair-wise　data　samples　to　train　the　fitting　model.　Experimental　results　on　authentic　distortions　datasets　and　synthetic　distortions　datasets　show　that　our　proposed　method　can　achieve　superior　performance　compared　with　the　state-of-the-art　methods.