Underwater　image　quality　assessment　(UIQA)　plays　a　crucial　role　in　monitoring　and　detecting　the　quality　of　acquired　underwater　images　in　underwater　imaging　systems.　Currently,　the　investigation　of　UIQA　encounters　two　major　challenges.　First,　a　lack　of　large-scale　UIQA　databases　for　benchmarking　UIQA　algorithms　remains,　which　greatly　restricts　the　development　of　UIQA　research.　The　other　limitation　is　that　there　is　a　shortage　of　effective　UIQA　methods　that　can　faithfully　predict　underwater　image　quality.　To　alleviate　these　two　challenges,　in　this　paper,　we　first　construct　a　large-scale　UIQA　database　(UIQD).　Specifically,　UIQD　contains　a　total　of　5369　authentic　underwater　images　that　span　abundant　underwater　scenes　and　typical　quality　degradation　conditions.　Extensive　subjective　experiments　are　executed　to　annotate　the　perceived　quality　of　the　underwater　images　in　UIQD.　Based　on　an　in-depth　analysis　of　underwater　image　characteristics,　we　further　establish　a　novel　baseline　UIQA　metric　that　integrates　channel　and　spatial　attention　mechanisms　and　a　transformer.　Channel-　and　spatial　attention　modules　are　used　to　capture　the　image　channel　and　local　quality　degradations,　while　the　transformer　module　characterizes　the　image　quality　from　a　global　perspective.　Multilayer　perception　is　employed　to　fuse　the　local　and　global　feature　representations　and　yield　the　image　quality　score.　Extensive　experiments　conducted　on　UIQD　demonstrate　that　the　proposed　UIQA　model　achieves　superior　prediction　performance　compared　with　the　state-of-the-art　UIQA　and　IQA　methods.