Different　from　the　natural　images　captured　from　real-world　scenes,　screen　content　images　(SCI)　are　synthetic　images　typically　composed　of　various　multimedia　contents,　such　as　computer-generated　text,　graphics,　and　animations.　Existing　SCI　quality　assessment　methods　usually　fail　to　fully　consider　the　impacts　of　image　edge　and　global　context　on　the　perceived　quality　of　screen　content　images.　To　address　the　above　issues,　this　paper　proposed　a　no-reference　screen　content　image　quality　assessment　model　based　on　edge　assistance　and　multi-scale　Transformer.　Firstly,　an　edge　structure　map　consisting　of　the　high-frequency　information　in　a　distorted　SCI　is　constructed　using　Gaussian　Laplace　operators.　Then　a　convolutional　neural　network　(CNN)　is　used　to　extract　and　fuse　the　multi-scale　features　from　the　input　distorted　SCI　and　the　corresponding　edge　structure　map,　thus　providing　additional　edge　information　gain　for　model　training.　In　addition,　this　paper　further　proposed　a　multi-scale　feature　encoding　module　based　on　Transformer　to　better　model　the　global　context　information　of　different　scale　images　and　edge　features　on　the　basis　of　the　local　features　obtained　by　CNN.　The　experimental　results　show　that　the　model　proposed　in　this　paper　outperforms　the　state-of-the-art　no-reference　and　full-reference　SCI　quality　assessment　methods,　and　achieves　higher　consistency　with　the　subjective　visual　perception.　©　2024　Chinese　Institute　of　Electronics.　All　rights　reserved.