Big　data　brings　more　challenges　to　image　quality　assessment　(IQA).　First　of　all,　reference　images　are　not　always　available,　so　full-reference　IQA　(FR-IQA)　and　reduced-reference　IQA　(RR-IQA)　can　be　problematic　for　big　data.　Because　of　no　requirement　for　any　pristine　knowledge,　no-reference　IQA　(NR-IQA)　has　recently　received　a　great　deal　of　attention.　In　recent　years,　deep　convolution　neural　network　(CNN)　has　achieved　many　successes　in　visual　recognition　tasks.　Researches　in　literature　have　shown　that　NR-IQA　metrics　can　be　learned　from　features　extracted　using　deep　CNN.　But　existing　CNN-based　IQA　models　focus　on　local　spatial　information　but　ignore　global　spatial　structure.　In　this　paper,　we　propose　a　NR-IQA　model　learns　from　multi-scale　CNN　(MSCNN).　Multi-scale　mechanism　has　boosted　the　performances　of　many　singe-scale　methods　in　a　variety　of　applications.　MSCNN　is　a　multi-scale　CNN　model　which　uses　fully　connected　layers　on　top　of　multiple　single-scale　CNN　models　which　responsible　for　feature　extraction　at　different　scales.　In　this　way,　the　features　extracted　at　multiple　scales　work　together　to　assess　image　quality.　Extensive　experiments　are　conducted　on　the　LIVE　database　and　the　experimental　results　validate　the　effectiveness　of　our　MSCNN　index　compared　to　typical　FR-IQA　and　state-of-the-art　NR-IQA　metrics.　Furthermore,　the　results　of　cross　database　experiments　show　that　MSCNN　has　good　generalization　ability.　©　2019,　Springer　Nature　Switzerland　AG.